thingies

2023-12-09 18:05:57 +01:00 · 2023-12-09 18:05:57 +01:00 · ee8dd41f85
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 | Week | Ready | Main item   |  S   | Classes                                                                                                                                                                | Student work                                                                                                                                                                                                                                                                                                |     |     |
 | ---- | ----- | ----------- | --- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- | --- |
 | 1    | Yes   | Python Prep |  N   | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies                                                  | - Python prep                                                                                                                                                                                                                                                                                               |     |     |
 | 2    |   Yes    |    Python Prep         |  N   | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation<br/> - L4: Simulation examples in Python. Introduction to case 1.                 | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1.                                           |     |     |
 | 3    |       | Case 1            |  N   | - L5: Simulation methodology. <br/> - L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here<br/> - S1: Workshop for case 1 | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics)                                                                                                              |     |     |
 | 4    |       | Case 1            |  Y   | - L7: Introduction to optimization<br/> - L8: Modeling optimization problems<br/> - S2: Workshop for case 1                                                            | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) |     |     |
 | 5    |       | Case 1/2            |  Y  | - L9: Taxonomy of optimization techniques <br/> - L10: Simulation-based optimization II. Introduction to case 2                                                        | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html)  |     |     |
 | 6    |       | Case 2            |  Y  | - L11: Challenges in real-world usage. Simulation vs Optimization <br/> - L12: Introduction to Machine Learning <br/> - S3: Workshop for case 2                        | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf)                                                                                                                                                                       |     |     |
 | 7    |       | Case 2/3            |  Y  | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2                                 | - Work on case 2 <br/> - Read case 3                                                                                                                                                                                                                                                                        |     |     |
 | 8    |       | Case 3            |  Y  | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3                                         | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ)                                                                                                            |     |     |
 | 9    |       | Case 3            |  Y  | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3                              | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0)                                                                                                                                                                                        |     |     |
 | 10   |       | Case 3            |  N  | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation                                                              | - Work on case 3                                                                                                                                                                                                                                                                                            |     |     |
 | 11   |       |             |     | - Exam                                                                                                                                                                 | - Deliver case 3                                                                                                                                                                                                                                                                                            |     |     |
--- a/aot/cases/case_1/case_1.zip
+++ b/aot/cases/case_1/case_1.zip
--- a/aot/cases/case_1/case_1_description.docx
+++ b/aot/cases/case_1/case_1_description.docx
--- a/aot/cases/case_1/case_1_description.pdf
+++ b/aot/cases/case_1/case_1_description.pdf
--- a/aot/cases/case_1/case_1_student_notebook.ipynb
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@ -0,0 +1,405 @@
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction to PuLP\n",
        "\n",
        "For case 1, you will need to define and solve optimization problems. In this notebook, I'll help you understand how to use `pulp`, a Python package for modeling optimization problems. You might want to check the following links:\n",
        "\n",
        "- Documentation: https://coin-or.github.io/pulp/\n",
        "- Homepage: https://github.com/coin-or/pulp\n",
        "\n"
      ],
      "metadata": {
        "id": "eLvjUuJdzS7z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing and checking all is in place"
      ],
      "metadata": {
        "id": "HFavOEVS0dbY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first thing you need to do is to install `pulp`. `pulp` is not in the standard available packages in Colab, so you need to run the following cell once. "
      ],
      "metadata": {
        "id": "HgZwpjUG0PsK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pulp"
      ],
      "metadata": {
        "id": "ni6Q_YiO0nIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "After doing that, you can import the library."
      ],
      "metadata": {
        "id": "k9YI0Kzw0qLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pulp"
      ],
      "metadata": {
        "id": "hw6keX7x0tZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If all is good, running the following command will print a large log testing `pulp`. The last line should read \"OK\"."
      ],
      "metadata": {
        "id": "vD_rXehL1KXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pulp.pulpTestAll()"
      ],
      "metadata": {
        "id": "Ney2a8mu1JqQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining and solving problems\n",
        "\n",
        "The following cells show you the absolute minimum to model and solve a problem with `pulp`. The steps are:\n",
        "\n",
        "1. Define decision variables\n",
        "2. Define the target function\n",
        "3. Define the constraints\n",
        "4. Assemble the problem\n",
        "5. Solve it\n",
        "6. Examine results\n",
        "\n",
        "For more flexibility, options and interesting stuff, please check up the PuLP documentation."
      ],
      "metadata": {
        "id": "oiXz40NR1whf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define decision variables"
      ],
      "metadata": {
        "id": "nq5bcQs03g0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = pulp.LpVariable(\n",
        "    name=\"x\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )\n",
        "\n",
        "y = pulp.LpVariable(\n",
        "    name=\"y\",\n",
        "    cat=pulp.LpInteger # This will make the variable integer only\n",
        "    )\n",
        "\n",
        "z = pulp.LpVariable(\n",
        "    name=\"z\",\n",
        "    cat=pulp.LpBinary # This will make the variable binary (only 0 or 1)\n",
        ")"
      ],
      "metadata": {
        "id": "0SPhww4L3buh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define the target function"
      ],
      "metadata": {
        "id": "uhlbq2oO35kp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target_function = 10 * x - 5 * y + z"
      ],
      "metadata": {
        "id": "pu3Im9DH39CN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define constraints"
      ],
      "metadata": {
        "id": "lqD0dD474Izw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "constraint_1 = x >= 0\n",
        "constraint_2 = y >= 0\n",
        "constraint_3 = x >= 10\n",
        "constraint_4 = y <= 50"
      ],
      "metadata": {
        "id": "5Cu51lYj4OUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assemble the problem\n",
        "\n",
        "To put all the parts together, you need to declare a problem and specify if you want to minimize or maximize the target function.\n",
        "\n",
        "Once you have that:\n",
        "- First, you \"add\" the target function.\n",
        "- After, you \"add\" all the constraints you want to include."
      ],
      "metadata": {
        "id": "d5nq94IM4kSU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "problem = pulp.LpProblem(\"my_silly_problem\", pulp.LpMinimize)\n",
        "\n",
        "problem += target_function\n",
        "\n",
        "for constraint in (\n",
        "    constraint_1,\n",
        "    constraint_2,\n",
        "    constraint_3,\n",
        "    constraint_4\n",
        "    ):\n",
        "  problem += constraint"
      ],
      "metadata": {
        "id": "yI-Oiwh64mRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Solve it\n",
        "\n",
        "The problem object is now unsolved. You can call the `solve` method on it to find a solution."
      ],
      "metadata": {
        "id": "RJTWfR8-5fBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f\"Status: {pulp.LpStatus[problem.status]}\"\n",
        "problem.solve()"
      ],
      "metadata": {
        "id": "4Fbltpbp5mRi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examine results\n",
        "\n",
        "After calling `solve` on a problem, you can access:\n",
        "- The status of the problem. It can be solved, but also it might show to be not feasible.\n",
        "- The values assigned to each decision variable.\n",
        "- The final value for the target function.\n",
        "\n"
      ],
      "metadata": {
        "id": "0pc9RmrO7FKo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Status: {pulp.LpStatus[problem.status]}\")\n",
        "for v in problem.variables():\n",
        "    print(v.name, \"=\", v.varValue)\n",
        "    \n",
        "print(pulp.value(problem.objective))"
      ],
      "metadata": {
        "id": "8U4xVvUg9W07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Peanut Butter Example\n",
        "\n",
        "As an additional example, you can find below the model and solver for the Peanut Butter Sandwich example we discussed on our lectures."
      ],
      "metadata": {
        "id": "I2lNaFm2XVK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pb = pulp.LpVariable(\n",
        "    name=\"Peanut Butter grams\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )\n",
        "\n",
        "b = pulp.LpVariable(\n",
        "    name=\"Bread grams\",\n",
        "    cat=pulp.LpContinuous \n",
        "    )"
      ],
      "metadata": {
        "id": "HI4E2dNoXVK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_function = 5.88 * pb + 2.87 * b"
      ],
      "metadata": {
        "id": "PfTxq8R0XVLB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_negative_pb = pb >= 0\n",
        "no_negative_b = b >= 0\n",
        "max_pb_we_have = pb <= 200\n",
        "max_b_we_have = b <= 300\n",
        "doctors_dietary_restriction = pb <= 0.13 * b"
      ],
      "metadata": {
        "id": "2X1AzQM8XVLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "problem = pulp.LpProblem(\"sandwich_problem\", pulp.LpMaximize)\n",
        "\n",
        "problem += target_function\n",
        "\n",
        "for constraint in (\n",
        "    no_negative_pb,\n",
        "    no_negative_b,\n",
        "    max_pb_we_have,\n",
        "    max_b_we_have,\n",
        "    doctors_dietary_restriction\n",
        "    ):\n",
        "  problem += constraint"
      ],
      "metadata": {
        "id": "3oEoQXebXVLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f\"Status: {pulp.LpStatus[problem.status]}\"\n",
        "problem.solve()\n",
        "print(f\"Status: {pulp.LpStatus[problem.status]}\")\n",
        "for v in problem.variables():\n",
        "    print(v.name, \"=\", v.varValue)\n",
        "    \n",
        "print(f\"Final calories: {pulp.value(problem.objective)}\")"
      ],
      "metadata": {
        "id": "u1vI73kiXVLF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Case 2\n",
        "\n",
        "You can use the rest of the notebook to work on the different parts of case 1."
      ],
      "metadata": {
        "id": "6kWgbTjU-LaN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Good luck!"
      ],
      "metadata": {
        "id": "aYzseTWh-Sal"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
 }
--- a/aot/cases/case_1/grading/case_1_grading.xlsx
+++ b/aot/cases/case_1/grading/case_1_grading.xlsx
--- a/aot/cases/case_1/grading/grading_team_1.xlsx
+++ b/aot/cases/case_1/grading/grading_team_1.xlsx
--- a/aot/cases/case_1/grading/grading_team_10.xlsx
+++ b/aot/cases/case_1/grading/grading_team_10.xlsx
--- a/aot/cases/case_1/grading/grading_team_11.xlsx
+++ b/aot/cases/case_1/grading/grading_team_11.xlsx
--- a/aot/cases/case_1/grading/grading_team_12.xlsx
+++ b/aot/cases/case_1/grading/grading_team_12.xlsx
--- a/aot/cases/case_1/grading/grading_team_2.xlsx
+++ b/aot/cases/case_1/grading/grading_team_2.xlsx
--- a/aot/cases/case_1/grading/grading_team_3.xlsx
+++ b/aot/cases/case_1/grading/grading_team_3.xlsx
--- a/aot/cases/case_1/grading/grading_team_5.xlsx
+++ b/aot/cases/case_1/grading/grading_team_5.xlsx
--- a/aot/cases/case_1/grading/grading_team_6.xlsx
+++ b/aot/cases/case_1/grading/grading_team_6.xlsx
--- a/aot/cases/case_1/grading/grading_team_8.xlsx
+++ b/aot/cases/case_1/grading/grading_team_8.xlsx
--- a/aot/cases/case_1/grading/grading_team_9.xlsx
+++ b/aot/cases/case_1/grading/grading_team_9.xlsx
--- a/aot/cases/case_1/prices_and_capacities.csv
+++ b/aot/cases/case_1/prices_and_capacities.csv
@ -0,0 +1,9 @@
 dock,40_ft_container_price_eur,max_capacity
 Rotterdam,470,33000
 Antwerp,470,25000
 Hamburg,480,44000
 Amsterdam,610,11000
 Marseille,380,9000
 Algeciras,280,20000
 Valencia,310,11000
 Genoa,340,7500
--- a/aot/cases/case_2/.Rhistory
+++ b/aot/cases/case_2/.Rhistory
--- a/aot/cases/case_2/case2_student_notebook.ipynb
+++ b/aot/cases/case_2/case2_student_notebook.ipynb
--- a/aot/cases/case_2/case_2.zip
+++ b/aot/cases/case_2/case_2.zip
--- a/aot/cases/case_2/case_2_description.docx
+++ b/aot/cases/case_2/case_2_description.docx
--- a/aot/cases/case_2/case_2_description.md
+++ b/aot/cases/case_2/case_2_description.md
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 # Case 1: Simulating Stock Policies
 - Title: Choosing stock policies under uncertainty
 - Description: Students role-play their participation as consultants in a
  project for Beanie Limited, a coffee beans roasting company. Elisa, the
  regional manager for the italian region, is not happy with their inventory
  policies for raw beans. The students are asked to analyse the problems posed
  by Elisa and apply simulation techniques, together with real data, to
  recommend a stock policy for the company's warehouse in the italian region.
  Python notebooks with some helpful prepared functions are provided to the
  students. The final delivery is a report with their recommendation to the
  client company, along with the used code.
 Stuff I want them to understand:
 - The model/hypothesis/validate
 - That in a simulation you set parameters, and you observe results
 - To write in a problem-solving manner.
 - That there are trade-offs and it's not trivial to find optimal solutions.
 Elements of the simulation:
 - Demand behavior
 - Lead time and standard deviation of provider (or providers)
 - Service level
 - Punishment for sales lost
 Observable effects of policies:
 - Mean inventory at hand
 - Service level
 - Warehousing/Capital Cost
 - Lost sales cost
 # Case 1: Choosing ordering goods under uncertainty
 You are part of an expert simulation team in SimiUPF SL. You have been assigned
 to a new project with a client company, Beanie Limited. Beanie Limited is a
 coffee roasting company and also distributes raw coffee beans through Europe
 and Middle East.
 Specifically, you will be working for Elisa Bolzano, the Director of Beanie
 Limited's warehouse located in Caserta, near Naples. Elisa is the full
 responsible for all the operations in the warehouse. She has requested the help
 of the SimiUPF team because she is worried about how certain things are managed
 in the warehouse and wants your help.
 The Caserta warehouse serves the raw coffee beans distribution business of
 Beanie Limited in southern europe and the mediterranean. The warehouse and its
 team are responsible for serving clients and also other smaller regional
 warehouses from Beanie Limited in this geography. From the warehouse point of
 view, they are usually just called "the clients". Whenever one of the clients
 needs raw beans, they arrange a transport truck that goes to the warehouse to
 pick up a certain amount of goods. Elisa's team fills up the truck with the
 requested goods, and then the clients take care of receiving that at their own
 locations. Given the size and relevance of the Caserta warehouse, activity is
 pretty much constant, with goods leaving the warehouse towards client locations
 every day.
 The Caserta warehouse itself has only one way to source coffee beans to store
 in their warehouse: requesting them to the Beanie Limited central offices in
 Diemen, near Amsterdam. Whenever Elisa's team considers that more stock is
 needed, they post a Purchase Order to the central office for a certain amount
 of beans. The central office arranges the goods and the delivery and, after a
 few days, the goods reach Caserta and are stored in the warehouse. The central
 office tries to ensure a lead time of 7 days (lead time is the time that passes
 between an order being placed and the goods reaching their destination), but
 the reality is they do what they can and this time is not always respected.
 Stock is a necessary evil (it implies a lot of cost), but Elisa's warehouse
 needs it to serve the clients in their region properly. Having too little stock
 means the clients need to wait long times to get their goods, which is risky
 for the business. On the other hand, having a lot of stock means high warehouse
 costs and financial opportunity cost (if Beanie Limited has 1 million € in
 coffee beans in a warehouse, that is 1 million € they can't invest somewhere
 else to improve their business). Thus, Elisa needs the stock to be as small as
 possible, while making sure she is not disappointing clients at the same time
 because of stockouts.
 Elisa is calling you because 2021 was a terrible year for the warehouse. The
 year was a chaotic one, and Elisa's team was not able to run operations
 smoothly. Although Elisa is not providing exact numbers, she is very well aware
 that the warehouse stock was unnecessarily high at times, and that there were
 too many periods where the warehouse was out of stock and clients had to wait
 to get their goods.
 Elisa thinks the main reason for this is the lack of a clear policy for when to
 order and how much to order from Diemen. Her team decides independently when to
 do it, and Elisa has a feeling that they are not approaching these decisions
 the right way. This means that sometimes they order when there is no need to,
 sometimes they don't order when they should, and that the amounts being ordered
 might not always be the optimal ones.
 Here is where you come in. As simulation experts, Elisa expects from you that
 you can help design an ordering policy to fix these issues. Doing this implies
 examining data from last year, building a proper simulation to examine the
 different factors being involved, and deciding when and how should Elisa's team
 order more goods from Diemen.
 Elisa expects a report where you share your findings and recommendations in a
 clear way that can help her team. Also, Elisa does not trust you blindly: you
 need to motivate the reasoning behind your recommendations. Otherwise, she will
 not feel comfortable implementing your recommendations and the bosses at
 SimiUPF will be mad at you...
 ## Detailed task definition
 - Below you will find four levels of questions. Levels 1 to 3 are compulsory.
  Level 4 is optional.
 - You need to write a report document where you answer the questions of the
  different levels. This report should be directed towards Elisa, should give
  her clear recommendations and should justify these recommendations. It's
  important for you to reflect your methodology to back your proposals.
 - Each level is worth 2 points out of a total of 10. The 2 missing points will
  grade the clarity and structure of your report and code.
 - You need to use a Python notebook to solve all levels. A helper notebook is
  provided. Please attach a notebook that shows your
  solution/proposal/analysis.
 - Include your team number, names and student IDs in all your deliverables.
 ## Data
 - You are provided with three tables that contain real data from 2021.
    - demand_events: this table shows how many beans left the Caserta warehouse
      to serve clients. There is some amount leaving every day because the
      warehouse serves many small orders from small clients, so there is always
      some order being fulfilled. The amount is measured in kilograms, and
      represents the total amount that left during that day. Individual orders
      are not relevant for this case, so we only look at daily total figures.
    - sourcing_events: this table shows the Purchase Orders that Elisa's team
      placed to Diemen. For each order, there are two dates: the date when
      Elisa's team placed the order, and the date where the beans actually
      reached the Caserta warehouse. The difference between those dates is the
      real lead time of each order. The amount is measured in kilograms.
    - stock_state: this table shows the stock at the warehouse at the end of
      each date. As you can guess, the stock for a certain date is the stock of
      the previous day, plus the goods that reached Caserta coming from Diemen,
      minus the goods that left the warehouse to serve client orders. A
      negative stock is not a challenge to the laws of physics: it means
      clients are waiting for their requested beans. If one row shows -1.000,
      it means that the warehouse is empty, and clients are awaiting a total
      amount of 1.000 kgs of beans. If next morning, a 1.000 kgs reach Caserta
      from Diemen, those will be used immediately to satisfy those awaiting
      clients, and the warehouse stock will become 0.
 ## Notebook
 A notebook with some helping code has been provided. The code contains a small
 simulation engine that can help you simulate a year of activity for the
 warehouse. The instructions on how to use the code are in the notebook itself.
 ## Levels
 - Level 1
    - Elisa wants you to measure the performance of the last year, providing
      quantitative metrics. She knows it was a bad year, but hasn't looked at
      the real data to summarize how bad it was. Remember that there is a
      trade-off: too much stock, is not desired, but running out of stock and
      making clients wait is also negative.
    - Going one step further, Elisa wants to know: what was done wrong?
 - Level 2
    - Elisa wants you to propose an ordering policy. This means, that you need
      to define a rule that, once each day, should answer the questions: should
      we place a Purchase Order to request beans from Diemen today? If yes, how
      much should we order?
    - Use simulation to present metrics on what is the expected performance
      with the policy you are proposing. Remember, you need to convince Elisa
      that this is better than what happens today.
    - As a specific constraint, Elisa explains that she wants that the
      probability of a stockout to be at most of 5% on any given day.
 - Level 3
    - Right after you finished designing your policy for level 2, Elisa called
      with some news: she has just been informed by the management in Diemen
      that a new Minimum Order Quantity (MOQ) rule will begin soon. This rule
      means that, when the Caserta warehouse places an order to request
      material from Diemen, the order should be of at least 500,000 kgs of
      beans, and not less than that.
    - Elisa wants you to take this into account. Does it affect the policy you
      proposed for level 2? If so, you need to come up with a new one that
      adapts to this rule and compare it to the previous one.
 - Level 4
    - Elisa briefly discussed with you in one meeting that there is an option
      to come to an agreement with the team in Diemen to improve the lead time
      stability. The proposal from Diemen is that, if the target lead time was
      set to something higher that the current 7 days target, providing a more
      stable delivery would be feasible.
    - The specific proposal from Diemen is: if the lead time target is changed
      to 15 days, they provide a 100% guarantee that orders will be delivered
      in exactly 15 days.
    - Elisa would love if you could take some additional time to study this
      proposal. What is better for Caserta? The current 7 days target
      lead-time, with unstable delivery times? Or a fixed, 15-day lead time?
    - The MOQ rule of level 3 still applies.
--- a/aot/cases/case_2/case_2_description.pdf
+++ b/aot/cases/case_2/case_2_description.pdf
--- a/aot/cases/case_2/demand_events.csv
+++ b/aot/cases/case_2/demand_events.csv
@ -0,0 +1,366 @@
 date,demand_quantity
 2021-01-01,54609.49281314914
 2021-01-02,36208.63648649295
 2021-01-03,77784.17276763407
 2021-01-04,76481.81360421646
 2021-01-05,52305.87658918292
 2021-01-06,57098.56436860317
 2021-01-07,41565.68706138541
 2021-01-08,81995.500619844
 2021-01-09,71041.91466404148
 2021-01-10,31787.17080818402
 2021-01-11,32735.09633866546
 2021-01-12,32855.44553254065
 2021-01-13,55420.934082626205
 2021-01-14,48883.311263507494
 2021-01-15,48368.597773147136
 2021-01-16,40225.99478591274
 2021-01-17,69003.66723779934
 2021-01-18,67378.93368511106
 2021-01-19,59444.432628854185
 2021-01-20,54441.80415596864
 2021-01-21,52796.814721541414
 2021-01-22,30193.150803735854
 2021-01-23,62328.53756562836
 2021-01-24,43690.320158519615
 2021-01-25,78451.89473980921
 2021-01-26,47794.13927746792
 2021-01-27,51454.93947489077
 2021-01-28,64633.17690683539
 2021-01-29,67371.66310250101
 2021-01-30,51137.068372905895
 2021-01-31,62192.931782584405
 2021-02-01,62381.245234820446
 2021-02-02,62744.03145531537
 2021-02-03,51305.706023572566
 2021-02-04,52618.66719247759
 2021-02-05,51961.10865929137
 2021-02-06,55154.27434352692
 2021-02-07,39799.62917632264
 2021-02-08,65486.97890826721
 2021-02-09,55355.23228947571
 2021-02-10,46211.4777291026
 2021-02-11,53132.95392507133
 2021-02-12,31537.03525349067
 2021-02-13,46447.72089889987
 2021-02-14,63731.03176553111
 2021-02-15,54454.77009849779
 2021-02-16,40659.50720269109
 2021-02-17,63493.99813149876
 2021-02-18,54931.266644895266
 2021-02-19,28278.734877540137
 2021-02-20,36379.638867181835
 2021-02-21,62202.758260545044
 2021-02-22,54523.210135004185
 2021-02-23,42395.85236943305
 2021-02-24,42934.425415725156
 2021-02-25,51494.77047631462
 2021-02-26,44220.2960470736
 2021-02-27,45670.120416197926
 2021-02-28,57107.49381367681
 2021-03-01,28972.234058115788
 2021-03-02,43048.734607813065
 2021-03-03,41505.53405595842
 2021-03-04,47926.03548243223
 2021-03-05,61278.99549030161
 2021-03-06,39044.50052424295
 2021-03-07,37142.63665375576
 2021-03-08,59385.01021647509
 2021-03-09,19622.861200135892
 2021-03-10,42875.82033258566
 2021-03-11,37298.094228973925
 2021-03-12,53411.899019061944
 2021-03-13,45345.99865109816
 2021-03-14,53211.40616195306
 2021-03-15,40974.40081655905
 2021-03-16,56025.67583148412
 2021-03-17,42957.88421097572
 2021-03-18,65464.99283743926
 2021-03-19,28628.77720679815
 2021-03-20,50873.13077669
 2021-03-21,47215.115350042746
 2021-03-22,44982.481462385775
 2021-03-23,72845.44784612038
 2021-03-24,36657.28355561715
 2021-03-25,35932.62440127316
 2021-03-26,90802.53749884429
 2021-03-27,54150.36198995029
 2021-03-28,57725.715294590715
 2021-03-29,49797.541628930994
 2021-03-30,39842.574327318325
 2021-03-31,46648.05822011224
 2021-04-01,38251.20061495644
 2021-04-02,54968.95147105346
 2021-04-03,39223.33668121346
 2021-04-04,55196.72314245463
 2021-04-05,60193.96623402014
 2021-04-06,107790.97235982082
 2021-04-07,58927.3553815537
 2021-04-08,52570.524217849554
 2021-04-09,40996.74684261808
 2021-04-10,52952.91853803685
 2021-04-11,55117.279622249654
 2021-04-12,62330.90239991735
 2021-04-13,60352.159875666686
 2021-04-14,46481.1929993728
 2021-04-15,26740.04853400801
 2021-04-16,58824.75809726865
 2021-04-17,48919.848176294996
 2021-04-18,69164.97343682637
 2021-04-19,65052.99346838036
 2021-04-20,86948.6316872793
 2021-04-21,47600.922050548594
 2021-04-22,53629.43407349051
 2021-04-23,58802.85640700405
 2021-04-24,27277.291629712032
 2021-04-25,72991.08369503866
 2021-04-26,46319.17825995694
 2021-04-27,42473.64434623195
 2021-04-28,62986.327912551824
 2021-04-29,59770.868769586974
 2021-04-30,46967.110213491585
 2021-05-01,31283.252270527257
 2021-05-02,45270.96133039481
 2021-05-03,40200.06151139432
 2021-05-04,28769.438869243786
 2021-05-05,41597.284397045456
 2021-05-06,42053.59694349442
 2021-05-07,32056.90063878994
 2021-05-08,24126.23251230451
 2021-05-09,61874.94040944404
 2021-05-10,69582.18210731493
 2021-05-11,54713.709988929106
 2021-05-12,77986.61766717135
 2021-05-13,51047.031274850284
 2021-05-14,59715.32807151039
 2021-05-15,73688.19223261088
 2021-05-16,65807.03078052355
 2021-05-17,55779.76069593255
 2021-05-18,53655.30817237868
 2021-05-19,53863.25586084147
 2021-05-20,42447.86518825701
 2021-05-21,34634.185379985654
 2021-05-22,81385.8091352819
 2021-05-23,53674.49856663084
 2021-05-24,32130.44754196027
 2021-05-25,35141.955123039676
 2021-05-26,35379.77494659018
 2021-05-27,47723.22357446625
 2021-05-28,50903.4531491154
 2021-05-29,52597.71388776773
 2021-05-30,37762.845725518426
 2021-05-31,31687.345250434668
 2021-06-01,1380.9898989639114
 2021-06-02,39384.958015718286
 2021-06-03,62407.74873554036
 2021-06-04,65856.83339328374
 2021-06-05,41159.52864583683
 2021-06-06,78292.78851815795
 2021-06-07,45085.067801033474
 2021-06-08,63969.20178674298
 2021-06-09,61865.47920564571
 2021-06-10,66481.65277980786
 2021-06-11,51663.83884564799
 2021-06-12,49462.60941335073
 2021-06-13,48907.56631014691
 2021-06-14,27822.17014448859
 2021-06-15,35277.37023428074
 2021-06-16,58563.35766039751
 2021-06-17,62202.644540009576
 2021-06-18,59361.79725578232
 2021-06-19,37617.54204811233
 2021-06-20,84719.87850010264
 2021-06-21,50153.4959152938
 2021-06-22,40323.203680923136
 2021-06-23,34962.05953043287
 2021-06-24,46613.355492701965
 2021-06-25,53246.878843729624
 2021-06-26,38693.95753463766
 2021-06-27,64529.674857993334
 2021-06-28,41126.42916746255
 2021-06-29,71984.7315338233
 2021-06-30,28888.043384351673
 2021-07-01,60293.9028556177
 2021-07-02,49816.29840729628
 2021-07-03,42812.38643232065
 2021-07-04,34909.73927750447
 2021-07-05,37591.53584671515
 2021-07-06,39469.203591839716
 2021-07-07,54396.08709948022
 2021-07-08,28047.275778018222
 2021-07-09,41834.25913212226
 2021-07-10,42604.985980117504
 2021-07-11,57706.78926368313
 2021-07-12,37965.84096167571
 2021-07-13,42719.54678256345
 2021-07-14,36541.18442771253
 2021-07-15,45179.2123752051
 2021-07-16,49107.119659072996
 2021-07-17,28547.87933059051
 2021-07-18,49714.75688145967
 2021-07-19,33936.612529083315
 2021-07-20,72130.34325424329
 2021-07-21,21218.431770514377
 2021-07-22,47114.58552828316
 2021-07-23,62864.89434803029
 2021-07-24,48265.27576417639
 2021-07-25,82856.83438714968
 2021-07-26,71619.09933599173
 2021-07-27,31313.91831932018
 2021-07-28,51702.76017876872
 2021-07-29,51759.91074963173
 2021-07-30,43399.332699545244
 2021-07-31,55424.54037571451
 2021-08-01,42225.94672589529
 2021-08-02,34807.53319498364
 2021-08-03,73469.6548372101
 2021-08-04,27209.4505106898
 2021-08-05,57237.08622864778
 2021-08-06,45169.07725691487
 2021-08-07,51028.44462209041
 2021-08-08,40990.419651217926
 2021-08-09,31432.767517597265
 2021-08-10,46704.92168243732
 2021-08-11,67447.45628232439
 2021-08-12,56060.762852218075
 2021-08-13,36096.04292632875
 2021-08-14,82159.16133987988
 2021-08-15,61384.53830739901
 2021-08-16,42921.0220131585
 2021-08-17,57574.80918470686
 2021-08-18,44223.76579375525
 2021-08-19,38112.188923509486
 2021-08-20,43302.275718994686
 2021-08-21,31995.553894163357
 2021-08-22,51456.16324022061
 2021-08-23,51376.41164803253
 2021-08-24,45624.593753100846
 2021-08-25,46734.78195159169
 2021-08-26,37565.07483616892
 2021-08-27,23554.39766955899
 2021-08-28,20604.948141803365
 2021-08-29,66334.25895451049
 2021-08-30,48047.85418448473
 2021-08-31,56191.71390404747
 2021-09-01,44859.282252098456
 2021-09-02,46030.147501430656
 2021-09-03,10703.823438653832
 2021-09-04,70343.60042856235
 2021-09-05,69607.14131423642
 2021-09-06,56931.55211394906
 2021-09-07,54663.61348397007
 2021-09-08,57725.529008129895
 2021-09-09,53757.39275518815
 2021-09-10,53480.74906036454
 2021-09-11,49602.291868261746
 2021-09-12,52985.895433602054
 2021-09-13,37411.73715166042
 2021-09-14,59155.55398150197
 2021-09-15,21991.02211112378
 2021-09-16,59508.78533477017
 2021-09-17,41544.13170794038
 2021-09-18,46878.16624464087
 2021-09-19,32469.82943570702
 2021-09-20,61806.26905613678
 2021-09-21,56074.725664414334
 2021-09-22,53483.80545741505
 2021-09-23,30077.209266523543
 2021-09-24,73070.54849698953
 2021-09-25,44118.377703017635
 2021-09-26,34134.3360656615
 2021-09-27,61619.510801440054
 2021-09-28,29334.959480643633
 2021-09-29,54840.778405071345
 2021-09-30,54861.25954092192
 2021-10-01,37666.69406650353
 2021-10-02,45514.88974301199
 2021-10-03,37872.595956602185
 2021-10-04,54862.49528732663
 2021-10-05,72168.41067112274
 2021-10-06,58411.76789552352
 2021-10-07,64450.64193866483
 2021-10-08,39846.169995410615
 2021-10-09,51012.92307031886
 2021-10-10,56193.97181413436
 2021-10-11,34065.44429410843
 2021-10-12,58206.460717550566
 2021-10-13,46487.945645762295
 2021-10-14,50076.70184963691
 2021-10-15,47580.71432500986
 2021-10-16,53915.82908269834
 2021-10-17,39606.35607109019
 2021-10-18,73790.25224218029
 2021-10-19,38407.621781936425
 2021-10-20,33404.97538990958
 2021-10-21,59931.9601178157
 2021-10-22,74486.16955897454
 2021-10-23,50195.02837816861
 2021-10-24,40073.20302847418
 2021-10-25,64626.796001266266
 2021-10-26,61727.34307665966
 2021-10-27,62187.88733591297
 2021-10-28,54214.87801602549
 2021-10-29,67142.34221772531
 2021-10-30,46487.69937914996
 2021-10-31,31086.740684974324
 2021-11-01,48278.95337799651
 2021-11-02,20718.68300716247
 2021-11-03,58138.40065378947
 2021-11-04,49722.30296011415
 2021-11-05,36742.138456983004
 2021-11-06,42736.48890700623
 2021-11-07,55356.6885726762
 2021-11-08,81832.3429551895
 2021-11-09,62338.173681547836
 2021-11-10,39202.33687407937
 2021-11-11,62786.50002194336
 2021-11-12,37726.689751497914
 2021-11-13,67033.48460270898
 2021-11-14,55366.81040522425
 2021-11-15,53898.24191372635
 2021-11-16,62845.981914852084
 2021-11-17,33833.82833106041
 2021-11-18,61076.99869993116
 2021-11-19,51228.112090794835
 2021-11-20,55635.47027518508
 2021-11-21,71803.01115735975
 2021-11-22,50315.05762449138
 2021-11-23,60710.007411381375
 2021-11-24,73249.01607526309
 2021-11-25,57699.01149670034
 2021-11-26,46798.292724322295
 2021-11-27,36359.18817807891
 2021-11-28,61511.52093729363
 2021-11-29,39284.72872960448
 2021-11-30,28462.06773230841
 2021-12-01,25809.261932155227
 2021-12-02,59425.1826389642
 2021-12-03,43014.05369644615
 2021-12-04,52769.507877984564
 2021-12-05,56657.291422193426
 2021-12-06,59175.14433261302
 2021-12-07,57450.71229516849
 2021-12-08,25887.75148158159
 2021-12-09,21300.79633013303
 2021-12-10,49686.47609053778
 2021-12-11,50683.5775985572
 2021-12-12,43090.41843560319
 2021-12-13,28815.44447997063
 2021-12-14,31445.73933682877
 2021-12-15,50964.200286431944
 2021-12-16,37689.76522472434
 2021-12-17,39270.444361100475
 2021-12-18,63767.92920582164
 2021-12-19,66245.76864762916
 2021-12-20,64310.02645239804
 2021-12-21,20186.46628098661
 2021-12-22,57829.12348425346
 2021-12-23,61209.40407684893
 2021-12-24,60229.294569424455
 2021-12-25,57790.197713617585
 2021-12-26,59848.30412950744
 2021-12-27,48843.47435878844
 2021-12-28,45483.444566160666
 2021-12-29,45361.81436223178
 2021-12-30,57103.88645952773
 2021-12-31,49479.32345442135
--- a/aot/cases/case_2/good_examples/Report_case2_Team#1.docx
+++ b/aot/cases/case_2/good_examples/Report_case2_Team#1.docx
--- a/aot/cases/case_2/good_examples/TEAM
+++ b/aot/cases/case_2/good_examples/TEAM
--- a/aot/cases/case_2/grading/case_2_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_10_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_10_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_11_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_11_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_12_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_12_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_1_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_1_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_2_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_2_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_3_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_3_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_5_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_5_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_5_grading_review_reply.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_5_grading_review_reply.xlsx
--- a/aot/cases/case_2/grading/case_2_team_6_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_6_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_8_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_8_grading.xlsx
--- a/aot/cases/case_2/grading/case_2_team_9_grading.xlsx
+++ b/aot/cases/case_2/grading/case_2_team_9_grading.xlsx
--- a/aot/cases/case_2/sourcing_events.csv
+++ b/aot/cases/case_2/sourcing_events.csv
@ -0,0 +1,61 @@
 request_date,delivery_date,amount
 2021-06-18,2021-06-24,361622.08421162824
 2021-04-08,2021-04-17,404943.20818378055
 2021-08-02,2021-08-10,372079.3749313439
 2021-03-23,2021-03-28,324410.8683704191
 2021-07-14,2021-07-19,467167.83305448893
 2021-03-02,2021-03-09,280731.9688885999
 2021-07-18,2021-07-26,369123.2301230304
 2021-02-18,2021-02-27,384645.34262920194
 2021-01-21,2021-01-25,310407.1921732673
 2021-08-12,2021-08-21,366174.22830054327
 2021-11-02,2021-11-09,391623.2685507731
 2021-07-13,2021-07-21,345458.2161390513
 2021-06-01,2021-06-10,360467.175317622
 2021-10-20,2021-10-31,336289.0502818366
 2021-07-21,2021-07-29,381876.17957110034
 2021-10-06,2021-10-11,314261.8504829489
 2021-11-15,2021-11-22,308806.6478588278
 2021-09-05,2021-09-14,330698.694265319
 2021-12-26,2022-01-04,407311.3676167535
 2021-01-16,2021-01-25,214247.55937424488
 2021-04-20,2021-04-26,269445.2778637154
 2021-01-22,2021-01-27,479246.85314207803
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--- a/aot/cases/case_2/stock_state.csv
+++ b/aot/cases/case_2/stock_state.csv
@ -0,0 +1,366 @@
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--- a/aot/cases/case_2/summary_first_sweep.csv
+++ b/aot/cases/case_2/summary_first_sweep.csv
@ -0,0 +1,10 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
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--- a/aot/cases/case_2/summary_second_sweep.csv
+++ b/aot/cases/case_2/summary_second_sweep.csv
@ -0,0 +1,26 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
 0.8,1.0,0.5629041095890411,0.5630136986301371,0.25173917949278124,100,-17208.65752949769,31032.43630715287,200730.55535889775,100,49.05,49.0,1.166666666666668,100
 0.8,1.05,0.804986301369863,0.8452054794520547,0.14385248604678155,100,155614.19545368513,176960.00812669177,89250.77498164216,100,47.39,47.0,0.7900262110470866,100
 0.8,1.1,0.8659726027397261,0.8876712328767123,0.09160908657556802,100,198680.3804320387,207071.20423920936,54616.27896318188,100,45.45,45.0,0.7017294652672376,100
 0.8,1.15,0.924082191780822,0.9342465753424658,0.042773373946112477,100,240782.04918482568,243459.64652624974,29223.097332241305,100,43.46,43.5,0.7305733185227711,100
 0.8,1.2,0.9414794520547946,0.9493150684931506,0.03451593530365504,100,261676.29869938738,264285.581551831,25131.022316170343,100,41.83,42.0,0.6971080231639825,100
 0.9,1.0,0.6648493150684932,0.6945205479452055,0.21570939170830844,100,73024.72198996953,104935.78104756039,163647.97407003478,100,49.32,49.0,0.8862587350511948,100
 0.9,1.05,0.8186575342465754,0.8753424657534246,0.16907143140798406,100,182114.7427397742,214236.05435841338,108978.10420990313,100,47.46,47.0,0.9147500624584167,100
 0.9,1.1,0.9226849315068493,0.9397260273972603,0.06860053019119008,100,257549.83941332222,264802.2368256551,46594.05059506942,100,45.65,46.0,0.7833494518006421,100
 0.9,1.15,0.9611506849315069,0.9726027397260274,0.040083238669095175,100,297073.0555063267,302993.9769790715,33827.59431750586,100,43.63,44.0,0.7057484028184599,100
 0.9,1.2,0.9742739726027397,0.9808219178082191,0.020594090521446923,100,316243.2068669456,319849.0246099904,23997.41545045925,100,42.05,42.0,0.6871842709362761,100
 1.0,1.0,0.6921643835616439,0.7698630136986302,0.2448481301495943,100,101882.22346645949,160026.78900577017,180014.0847194441,100,49.41,49.0,1.0739806066379143,100
 1.0,1.05,0.8867945205479453,0.936986301369863,0.1266759950750295,100,246218.68552190147,273469.8906422792,85390.22913704212,100,47.72,48.0,0.7923880286064316,100
 1.0,1.1,0.9432054794520548,0.9698630136986301,0.07141874718689835,100,301924.2700638766,316299.9013534653,59963.75704883238,100,45.76,46.0,0.7123726184201268,100
 1.0,1.15,0.9764109589041096,0.9863013698630136,0.02872101571594946,100,340290.7186400273,343600.27244723897,34849.39399657999,100,43.88,44.0,0.7286350876186868,100
 1.0,1.2,0.9906575342465753,0.9945205479452055,0.0113001726894409,100,369036.41037286026,371895.01996879186,24001.498220542777,100,42.05,42.0,0.6256309946079575,100
 1.1,1.0,0.7282465753424657,0.7479452054794521,0.2260275046018253,100,139083.56089769275,162758.51029021217,168922.45751711642,100,49.45,49.5,1.0576799462440758,100
 1.1,1.05,0.9035068493150685,0.9671232876712329,0.16213843415295967,100,289746.11535166804,320941.33405487105,114866.00516221055,100,47.71,48.0,0.7951240294584386,100
 1.1,1.1,0.9613698630136986,0.989041095890411,0.0655565291501118,100,350031.4173621522,367119.16888596804,60281.0758667702,100,45.9,46.0,0.7719841941125448,100
 1.1,1.15,0.9912054794520548,0.9972602739726028,0.017645347308402025,100,396603.9442134802,400346.3145094304,29483.57515994371,100,44.0,44.0,0.7247430753394793,100
 1.1,1.2,0.9956164383561643,1.0,0.007052473418545561,100,417138.91165137023,418615.9689806167,23055.605401908946,100,42.1,42.0,0.6741998624632427,100
 1.2,1.0,0.7846575342465754,0.8698630136986301,0.2252867428579435,100,193695.8595228242,242047.6119066357,194075.09374543617,100,49.79,50.0,1.121822110891404,100
 1.2,1.05,0.9479178082191781,0.9780821917808219,0.09026491930718351,100,346443.9102815047,358689.5454693798,87392.79621129965,100,48.05,48.0,0.7436600722307887,100
 1.2,1.1,0.9872602739726026,0.9972602739726028,0.025089991771959474,100,411297.4581000472,416474.64406344807,44380.80426082012,100,46.0,46.0,0.7106690545187017,100
 1.2,1.15,0.9916438356164383,1.0,0.022282747753160106,100,441406.6727818038,448100.49807167007,38234.435371301166,100,44.19,44.0,0.7063206700139026,100
 1.2,1.2,0.9969589041095891,1.0,0.007179214412726819,100,468382.008571895,474174.00471737224,28487.211871335898,100,42.35,42.0,0.6723244767373897,100
--- a/aot/cases/case_2/summary_third_sweep.csv
+++ b/aot/cases/case_2/summary_third_sweep.csv
@ -0,0 +1,71 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
 0.8,1.05,0.7859726027397261,0.8260273972602741,0.1598362225708639,100,144208.47823315178,169318.30356595665,100514.74254469664,100,47.4,47.0,0.8164965809277261,100
 0.8,1.06,0.794958904109589,0.8301369863013699,0.1403919914831539,100,153586.2551041631,173418.410864591,78290.88655784576,100,47.11,47.0,0.7371114795831993,100
 0.8,1.07,0.8067671232876712,0.8315068493150686,0.1339955549137507,100,161441.590865957,172098.93199610294,72690.59543201819,100,46.56,47.0,0.7291893935852132,100
 0.8,1.08,0.8381917808219178,0.8493150684931507,0.10243407881742274,100,180757.48462696798,185912.87023764107,63117.92391789514,100,46.47,46.0,0.7843803447642761,100
 0.8,1.09,0.8667671232876712,0.8808219178082193,0.08505385831720369,100,198896.08671044532,205127.32601251808,47198.21828806646,100,45.77,46.0,0.664466064983872,100
 0.8,1.1,0.878027397260274,0.9054794520547945,0.09679462977911307,100,207399.79024280803,225251.01385664742,55542.82909581238,100,45.52,46.0,0.7032392583932264,100
 0.8,1.11,0.8961643835616439,0.9123287671232877,0.06811908370060707,100,218048.13332393923,226635.2932288471,41083.817034265245,100,45.0,45.0,0.7654139963827331,100
 0.8,1.12,0.9054246575342466,0.9178082191780822,0.05473703917338135,100,226233.79870989014,228435.7464824344,33790.75003378518,100,44.75,45.0,0.770346898114313,100
 0.8,1.13,0.9113972602739726,0.9219178082191781,0.057722017942900286,100,229820.05176361586,234205.64538325084,37180.29105911812,100,44.41,44.0,0.6371495868001452,100
 0.8,1.14,0.9096164383561645,0.9260273972602739,0.06476061395141362,100,229451.0061609741,236898.16887785456,39996.703715484575,100,43.98,44.0,0.6192207550093444,100
 0.9,1.05,0.799068493150685,0.8589041095890411,0.17478733730560359,100,170856.8688515813,195706.0585029618,107559.81482477198,100,47.66,48.0,0.7278028371042323,100
 0.9,1.06,0.8728493150684932,0.9178082191780822,0.14035816656132336,100,214583.50564701803,237766.20853748685,91537.75933622228,100,47.16,47.0,0.9180567971690441,100
 0.9,1.07,0.8863835616438357,0.9246575342465753,0.13202268131162476,100,225962.8131575521,248956.4255873827,87091.59551249178,100,46.85,47.0,0.7703468981143126,100
 0.9,1.08,0.8878356164383561,0.9109589041095891,0.1034915126220675,100,231793.74733914566,241230.59100630687,65364.238707012075,100,46.37,46.0,0.7608474807008903,100
 0.9,1.09,0.910958904109589,0.9342465753424658,0.0848895391251729,100,247414.19854095648,259646.62831328082,56859.363211731616,100,46.14,46.0,0.829019135649309,100
 0.9,1.1,0.9097808219178083,0.936986301369863,0.09263505453625975,100,248051.95584330414,261613.48309493947,62298.32416064076,100,45.61,46.0,0.8027100562117265,100
 0.9,1.11,0.9249041095890411,0.9493150684931506,0.06777942231931827,100,259875.5154301237,265650.987470857,44837.543279018144,100,45.24,45.0,0.7123726184201262,100
 0.9,1.12,0.9426575342465753,0.958904109589041,0.054991006506983194,100,276332.38956547884,283931.61956175056,40395.41216420654,100,44.78,45.0,0.7327821620165825,100
 0.9,1.13,0.9434794520547944,0.9657534246575343,0.05636688915712585,100,277312.20724561514,285598.0109710443,42114.061861274546,100,44.54,44.0,0.6422812876966388,100
 0.9,1.14,0.9461369863013698,0.9671232876712329,0.05949943242463646,100,281586.1900728949,292772.94372766616,44568.637852341606,100,44.05,44.0,0.7436600722307894,100
 0.95,1.05,0.8504657534246576,0.8876712328767123,0.1426216962196151,100,209502.91100042395,231229.60137442837,98473.14510338863,100,47.62,48.0,0.7885544888073256,100
 0.95,1.06,0.8763561643835617,0.9205479452054794,0.13371982345233996,100,232132.86769214022,253984.80407355353,85226.62571623079,100,47.18,47.0,0.7571877794400369,100
 0.95,1.07,0.8897534246575343,0.9232876712328767,0.11000276133429772,100,244444.98080390875,258917.87933101476,70323.6063035861,100,46.75,47.0,0.7833494518006393,100
 0.95,1.08,0.9206301369863015,0.958904109589041,0.09976094450434589,100,265926.47402984084,283736.9000909083,66614.08688632757,100,46.37,46.0,0.812217316792107,100
 0.95,1.09,0.9294794520547945,0.958904109589041,0.08543716778698167,100,274030.97554696375,285147.23292353324,56892.76092575346,100,46.01,46.0,0.7452882297839849,100
 0.95,1.1,0.9287123287671233,0.9547945205479451,0.08003250087101145,100,274078.3527874601,283856.4572754771,56690.76457159778,100,45.64,46.0,0.6744994402884967,100
 0.95,1.11,0.9364383561643835,0.9561643835616438,0.06526268605058627,100,280881.6829739677,296677.6782650106,49506.88718738129,100,45.34,45.0,0.6391281940932124,100
 0.95,1.12,0.9556438356164384,0.973972602739726,0.07386818268433056,100,298727.4002688783,311051.6643517049,55230.95325025695,100,44.94,45.0,0.6327748733981471,100
 0.95,1.13,0.9625479452054795,0.9753424657534246,0.03853770121887277,100,303624.8432092603,305964.4471615079,33997.45186113061,100,44.56,45.0,0.6562827673397108,100
 0.95,1.14,0.965041095890411,0.9808219178082191,0.04503533313503058,100,309887.30047959444,316649.4679522059,37629.163123648126,100,44.27,44.0,0.6942039987778751,100
 1.0,1.05,0.878054794520548,0.9232876712328767,0.12456329754626162,100,239357.93264631205,263694.2998236382,80089.22781996857,100,47.79,48.0,0.8680059581790576,100
 1.0,1.06,0.8947123287671234,0.941095890410959,0.1355028323290669,100,255749.40900901722,285246.23294684856,94956.97950416044,100,47.34,47.0,0.7683118053909035,100
 1.0,1.07,0.9047945205479452,0.9438356164383561,0.13250530459875723,100,264039.6231237091,286009.00904912734,90347.19839737378,100,46.86,47.0,0.7787584005740479,100
 1.0,1.08,0.9243287671232876,0.952054794520548,0.08231970120182112,100,281449.77736003743,293460.12722831377,61529.54372494277,100,46.59,47.0,0.726065917924618,100
 1.0,1.09,0.9343013698630136,0.9643835616438357,0.08847012172074435,100,291842.87457335443,304680.33844471246,67944.88567969218,100,45.95,46.0,0.74366007223079,100
 1.0,1.1,0.9562739726027396,0.9753424657534246,0.05972417397215357,100,312685.41726779164,317915.94000983867,49197.89880376956,100,45.67,46.0,0.7792122414154682,100
 1.0,1.11,0.968958904109589,0.9808219178082191,0.03477533957582285,100,323801.7037949509,331782.3224302352,37926.45459276231,100,45.43,45.0,0.781800563578795,100
 1.0,1.12,0.9685479452054794,0.9835616438356164,0.04226908937517442,100,325205.9243724567,337201.9597184559,45250.733446382925,100,44.9,45.0,0.6890192121758836,100
 1.0,1.13,0.9677808219178082,0.9808219178082191,0.04733685887394322,100,330657.6603956923,337075.92965660174,42421.01611114322,100,44.6,45.0,0.7247430753394793,100
 1.0,1.14,0.9823013698630138,0.9917808219178083,0.02489902195487209,100,346636.5895811218,351643.50356452586,32724.192897340898,100,44.11,44.0,0.6947857747012907,100
 1.05,1.05,0.9096712328767123,0.9506849315068493,0.1268551724970295,100,275036.8994307617,290241.47330518905,92189.74314580947,100,47.76,48.0,0.8542230210530327,100
 1.05,1.06,0.9146575342465754,0.9561643835616438,0.12009700618062043,100,283383.00552277366,305048.07351176,96246.65543086598,100,47.27,47.0,0.7365631354644712,100
 1.05,1.07,0.9413972602739726,0.9698630136986301,0.09003060152571733,100,303722.93403703946,313331.6029302139,73057.08547382594,100,46.91,47.0,0.7533762390570582,100
 1.05,1.08,0.9469315068493152,0.9780821917808219,0.0793003279699756,100,317055.4136440441,329838.54958045797,63485.26929871185,100,46.59,47.0,0.7666666666666667,100
 1.05,1.09,0.9626575342465753,0.9835616438356164,0.050483002706923574,100,327783.50728198123,337811.29494201415,48868.03042293043,100,46.26,46.0,0.7603826787755086,100
 1.05,1.1,0.9624109589041097,0.9794520547945205,0.05435562429151819,100,331620.04862504784,339715.6067690905,51905.17082289278,100,45.78,46.0,0.7464393593398702,100
 1.05,1.11,0.9697534246575343,0.989041095890411,0.04971307496436453,100,340227.99533744523,351120.098823712,49956.8593361718,100,45.43,45.0,0.7420283421851632,100
 1.05,1.12,0.9702739726027397,0.9863013698630136,0.05158788657894017,100,343659.58516506077,353342.32752993377,44689.97958762292,100,45.05,45.0,0.7299508769967222,100
 1.05,1.13,0.9841643835616438,0.9917808219178083,0.021220325968317567,100,362923.71636342397,367782.8510066926,30511.492738523157,100,44.68,45.0,0.7089613971340485,100
 1.05,1.14,0.9803287671232878,0.989041095890411,0.03439019008566572,100,358818.66979266173,366030.73615185026,38779.97603060032,100,44.42,44.0,0.6693883835866954,100
 1.1,1.05,0.8774520547945205,0.9493150684931506,0.16332896109997266,100,262116.4533938322,307218.83635365096,137001.0438185493,100,47.65,48.0,0.845367650579594,100
 1.1,1.06,0.93,0.9575342465753425,0.08998299067660721,100,308184.9288191942,315262.4431269691,76531.77678714157,100,47.33,47.0,0.7114503609961922,100
 1.1,1.07,0.9332054794520548,0.9767123287671233,0.1133241844881452,100,316339.8582983121,343388.79801796563,88723.83399772066,100,47.18,47.0,0.7704124567628391,100
 1.1,1.08,0.9466849315068494,0.9767123287671233,0.09839032697603153,100,331184.26416084,349136.8427766626,81927.62325661813,100,46.52,47.0,0.8346050901867643,100
 1.1,1.09,0.9556164383561644,0.989041095890411,0.10547415073320952,100,344293.7967364825,363724.13265618257,85256.01192642892,100,46.26,46.0,0.7333333333333325,100
 1.1,1.1,0.9733150684931507,0.9945205479452055,0.06351837655542099,100,363147.94176298834,374326.82103307196,57145.65533432398,100,45.75,46.0,0.6256309946079563,100
 1.1,1.11,0.9746849315068493,0.9917808219178083,0.04402911492630584,100,363388.1927891669,373491.37867364136,49274.54501585131,100,45.5,45.0,0.6435381994422805,100
 1.1,1.12,0.9793972602739726,0.9917808219178083,0.03915141511936552,100,370890.05170730536,376338.38252097333,44302.65600471223,100,45.1,45.0,0.7035264706814484,100
 1.1,1.13,0.9804109589041096,0.9945205479452055,0.03243412017637617,100,377327.82974791917,381348.09184570936,38488.17279855358,100,44.76,45.0,0.7123726184201263,100
 1.1,1.14,0.9886849315068492,0.9945205479452055,0.017334789000443024,100,384809.10717216856,390844.7511152257,31093.991487524258,100,44.51,44.0,0.6589707309451781,100
 1.2,1.05,0.9171780821917808,0.9726027397260274,0.13067274741673798,100,322060.45665888296,340538.9200925699,105710.6766863121,100,47.92,48.0,0.8490041700769674,100
 1.2,1.06,0.9489041095890411,0.9917808219178083,0.09929085079019878,100,355803.9539068448,384192.0333909353,97019.18401447838,100,47.61,48.0,0.7771353768420244,100
 1.2,1.07,0.9564109589041097,0.9917808219178083,0.07656699649287621,100,364057.84548111144,389141.36283888033,75902.53849836365,100,47.31,47.0,0.6918720031095832,100
 1.2,1.08,0.9602465753424657,0.989041095890411,0.08505995544913891,100,373446.1741382272,386689.89061583555,83449.85958080231,100,46.81,47.0,0.6918720031095821,100
 1.2,1.09,0.9672054794520548,0.9945205479452055,0.08413140089715565,100,390837.72881569655,407689.56891719927,79174.94776761712,100,46.26,46.0,0.7194273817250479,100
 1.2,1.1,0.9738356164383561,0.9972602739726028,0.060117001280751294,100,398871.1618030684,414081.79698469874,60975.215674990715,100,46.23,46.0,0.6794977348874467,100
 1.2,1.11,0.9765205479452055,0.9972602739726028,0.05166994864627592,100,404329.81311479496,423147.71118130337,66661.79450110722,100,45.64,46.0,0.6744994402884964,100
 1.2,1.12,0.9849315068493151,0.9972602739726028,0.04728618617716756,100,424054.9128070699,431696.0971162426,53638.952963032265,100,45.25,45.0,0.7436600722307911,100
 1.2,1.13,0.991041095890411,0.9986301369863013,0.018392476408138413,100,428392.03453004366,434494.4380003613,39705.52165661862,100,44.91,45.0,0.6528105515090998,100
 1.2,1.14,0.992986301369863,1.0,0.01664151932365369,100,438627.18168542744,445798.6518808934,35926.80443305188,100,44.45,44.0,0.6871842709362769,100
--- a/aot/cases/case_3/case_3.zip
+++ b/aot/cases/case_3/case_3.zip
--- a/aot/cases/case_3/case_3_description.docx
+++ b/aot/cases/case_3/case_3_description.docx
--- a/aot/cases/case_3/case_3_description.md
+++ b/aot/cases/case_3/case_3_description.md
@ -0,0 +1,175 @@
 # Case 3: Improving last mile logistics with Machine Learning
 After your last engagement, you have pretty much become the go-to
 service provider for advanced quantitative methods. Congratulations!
 You have been called again to help a different manager within Beanie Limited:
 Estefania Pelaez. Estefania is the city manager for Barcelona. She is in charge
 of all commercial and logistic operations that happen in the city.
 One of the operations that Beanie Limited runs in Barcelona is their own
 last-mile coffee delivery service. The company runs a small fleet of vans and
 trucks that delivers small orders of roasted coffee beans (typically,
 around 10-100kg of coffee per delivery) to restaurants, cafes, hotels and other
 businesses in the city.
 The efficiency of the deliveries is important to keep margins profitable for
 Beanie Limited. A sloppy management can make the company lose money. Hence,
 Estefania is always working on ways to make the operations as smooth as
 possible.
 Currently, Beanie Limited has rented space in two warehouses: one located in
 Zona Franca and another one in Baro de Viver. Complementing that, the company
 has a small fleet of combi vans, regular sized vans and one truck, which are
 used by Beanie Limited own drivers to deliver the coffee beans from the
 warehouses to the customer's facilities.
 Orders placed by the customers are predictable and placed with time in advance,
 which allows Estefania and her team to plan the deliveries and minimize wasted
 effort by the fleet. Since they know which locations they will need to deliver
 to, they use a routing software that drafts the routes that each vehicle will
 cover each day.
 Recently, Estefania recently realized something: deliveries are almost always
 taking place too early or too late. After researching with some data, Estefania
 found out that there was nothing wrong with the routing software time
 estimates: the driving time between locations predicted by the software is
 accurate. The real issue is related to what Estefania's team calls the "
 engine-off" time.
 The engine-off time is the time a driver spends actually dropping off goods in
 a client location. It's called engine-off because the clock starts ticking when
 the driver takes the keys off the van and stops when the driver starts driving
 again.
 Currently, Estefania and her team assume an engine-off time of 3 minutes for
 all deliveries when building the delivery routes and schedules. But it seems
 that this not realistic at all and is causing a lot of trouble with the
 schedules. Clients are not happy with delivery times not being respected, some
 driver routes end up too early (which means that the same driver could have
 covered more clients) and some others run for too long (which means they have
 to go back to the warehouse without delivering all the goods requested by the
 clients).
 If Estefania could know beforehand what would be the engine-off time of
 different deliveries, she could improve the route planning to fix all of these
 issues. She has been told that Machine Learning could help with this and is
 expecting you to find out if and how it can be applied to this problem.
 ## Detailed Task Definition
 - Below you will find tasks organized in four levels. 
 - You need to write a report document where you answer the questions of the
  different levels. This report should be directed towards Estefania, should
  give her clear recommendations and should justify these recommendations. It's
  important for you to reflect your methodology to back your proposals.
 - Each level is worth 2 points out of a total of 10. The 2 missing points will
  grade the clarity and structure of your report.
 - You need to use a Python notebook to solve all levels. Please attach a
  notebook that shows your solution/proposal/analysis. Your notebook should be
  runnable "as-is". That means that anyone should be able to run it from
  beginning to end without any additional instructions or action required (
  except for uploading data from a CSV in the Google Colab environment. That
  requires someone to upload the file with a few clicks and it's fine).
 - Include your team number, names and student IDs in all your deliverables.
 ## Data
 By joining the customer database together with past deliveries details,
 Estefania has built a dataset of execute deliveries. The table contains 9,000
 examples of past deliveries and their engine-off times. The exact field
 meanings are explained below:
 - client_name: the name of the client.
 - truck_size: what type of truck was being used. Can be one of Combi, Van or
  Truck.
 - truck_origin_warehouse: from which Beanie Limited warehouse did the route
  start.
 - delivery_timestamp: at what date and time was the delivery done (defined as
  the moment the engine-off time starts).
 - total_weight: total weight of the goods delivery.
 - brand_1_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #1.
 - brand_2_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #2.
 - brand_3_coffee_proportion: what percentage of the delivery was of Beanie's
  brand #3.
 - driver_id: the ID of the driver that was driving the route.
 - is_fresh_client: whether the client was fresh at the date of the delivery.
  Fresh clients are clients that have been doing business with Beanie for less
  than 30 days.
 - postcode: the postcode of the client location.
 - business_category: whether the client is a hotel, a cafe or restaurant or a
  coffee retailer.
 - floor: the physical position of the client location.
 - partnership_level: indicates the partnership level with Beanie. Key Account
  are important clients for Beanie Limited. Diamond clients are the top
  priority clients for the company.
 - box_count: how many distinct boxes were delivered to the client. The coffee
  beans bags are grouped into boxes for delivery.
 - final_time: the engine-off time, measured in seconds.
 ## Notebook
 Case 3 comes with no helping notebook: this time, you will have to code things
 from scratch yourselves. Remember that you are still suposed to write and
 deliver a notebook (see the "Detailed Task Definition" section).
 A few comments on your notebook:
 - I'm a going to constraint you to use [scikit-learn](https://scikit-learn.org/stable/) as a ML library. You can
  of course use other useful Python libraries such as pandas, numpy, etc. But
  for ML modeling, please go with scikit-learn.
 - Below you can find some useful materials which relate to what you need to do
  as part of the case:
    - [A simple, guided EDA on the Titanic Dataset](https://www.datacamp.com/tutorial/kaggle-machine-learning-eda)
    - [A guide on regression performance metrics](https://machinelearningmastery.com/regression-metrics-for-machine-learning/)
      and some [material from scikit-learn on the same topic](https://scikit-learn.org/stable/modules/classes.html#regression-metrics)
    - An [introduction to cross-validation](https://machinelearningmastery.com/k-fold-cross-validation/)
    - A thorough [review on why we need to use baselines](https://blog.ml.cmu.edu/2020/08/31/3-baselines/) in ML
    - A simple [introduction to linear regression with scikit-learn](https://stackabuse.com/linear-regression-in-python-with-scikit-learn/)
 ## Levels
 ### Level 1
 - Assess for Estefania if ML is a good choice for her problem and explain why.
 - Perform Exploratory Data Analysis on the given data. Is it clean? Which
  variables could be useful to explain the engine-off time? Are there any other
  interesting things you can draw from the dataset?
 ### Level 2
 - Present how are you going to measure performance for this problem and how you
  will use the available data for testing it.
 - Develop a baseline algorithm and evaluate its performance.
 ### Level 3
 - Develop the best model you can make to predict engine-off time.
 - Explain your methodology and report on performance.
 - Compare your performance to the baseline algorithm. Reflect on what is the
  cause of whatever differences can be observed between both.
 ### Level 4
 After presenting your model and results, Estefania has two different questions:
 - Estefania would like to learn from the ML algorithm. What are the most
  relevant features that define the engine-off time? Can you somehow quantify
  how important each is or which are most useful?
 - Estefania is interested in learning about next steps. What can be done to
  improve even more the model performance and achieve better results?
 ### SPECIAL
 For this case, we are going to run a little competition. There will be a
 surprise gift on the last lecture for the team that wins.
 The competition consists on getting the best performing model of the course. I
 have a hidden part of Estefania's dataset. Besides the data for the case, I have
 also added an additional thousand records for which you don't have the engine off 
 time (so you can make predictions, but you can't compare them against the true 
 result). 
 To enter the competition, you will have to send me a csv with a single column named "predictions". Each row should be a record of the extra dataset, delivered in the same order as the predictive features you received. I will measure the RMSE of all deliveries and the team with the lowest RMSE will be the winner.
--- a/aot/cases/case_3/case_3_description.pdf
+++ b/aot/cases/case_3/case_3_description.pdf
--- a/aot/cases/case_3/competition/beanie_limited_predictions.csv
+++ b/aot/cases/case_3/competition/beanie_limited_predictions.csv
--- a/aot/cases/case_3/competition/ground_truth.csv
+++ b/aot/cases/case_3/competition/ground_truth.csv
--- a/aot/cases/case_3/competition/results.txt
+++ b/aot/cases/case_3/competition/results.txt
@ -0,0 +1,7 @@
 METRIC: RMSE
 team_12 RMSE: 96.45072068592552
 team_2 RMSE: 112.43239178382082
 team_3 RMSE: 140.60987164535905
 team_1 RMSE: 156.38651077473006
 team_5 RMSE: 159.27300996653568
 beanie_limited RMSE: 396.84362604059373
--- a/aot/cases/case_3/competition/team_12_predictions.csv
+++ b/aot/cases/case_3/competition/team_12_predictions.csv
--- a/aot/cases/case_3/competition/team_1_predictions.csv
+++ b/aot/cases/case_3/competition/team_1_predictions.csv
--- a/aot/cases/case_3/competition/team_2_predictions.csv
+++ b/aot/cases/case_3/competition/team_2_predictions.csv
--- a/aot/cases/case_3/competition/team_3_predictions.csv
+++ b/aot/cases/case_3/competition/team_3_predictions.csv
--- a/aot/cases/case_3/competition/team_5_predictions.csv
+++ b/aot/cases/case_3/competition/team_5_predictions.csv
--- a/aot/cases/case_3/competition_dropoffs_df.csv
+++ b/aot/cases/case_3/competition_dropoffs_df.csv
--- a/aot/cases/case_3/competition_dropoffs_df_only_x.csv
+++ b/aot/cases/case_3/competition_dropoffs_df_only_x.csv
--- a/aot/cases/case_3/dropoffs_df.csv
+++ b/aot/cases/case_3/dropoffs_df.csv
--- a/aot/cases/case_3/full_dropoffs_df.csv
+++ b/aot/cases/case_3/full_dropoffs_df.csv
--- a/aot/cases/case_3/grading/grading.xlsx
+++ b/aot/cases/case_3/grading/grading.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_1.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_1.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_10.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_10.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_11.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_11.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_12.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_12.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_2.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_2.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_3.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_3.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_5.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_5.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_6.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_6.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_8.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_8.xlsx
--- a/aot/cases/case_3/grading/grading_case3_team_9.xlsx
+++ b/aot/cases/case_3/grading/grading_case3_team_9.xlsx
--- a/aot/exam/exam.odt
+++ b/aot/exam/exam.odt
--- a/aot/exam/exam.pdf
+++ b/aot/exam/exam.pdf
--- a/aot/exam/grading.ods
+++ b/aot/exam/grading.ods
--- a/aot/exam/retake_exam.odt
+++ b/aot/exam/retake_exam.odt
--- a/aot/exam/retake_exam.pdf
+++ b/aot/exam/retake_exam.pdf
--- a/Presentation.md
+++ b/Presentation.md
@ -0,0 +1,115 @@
 - On 16/11/22, there is going to be an "Electives Day" where we will present our electives to the MSc students so they are better informed before they can make their choices.
 - It's my time to shine. There are few students, so if not enough of them enroll in the course, they will cancel my course.
 Agenda to cover:
 - Present myself
 	- Education
 	- Experience
 	- The weird bits
 	- Open source my evaluation
 - Present the course
 	- Funny example
 	- Contents
 	- Purpose
 	- Methodology
 - Provide contact
 	- QR code to my email
 	- QR code to my evaluations
 ---
 # Message for the students
 Hi, my name is Pablo and I'm the teacher in charge of the course Practical Data Science for Operations. The goal of this text is to seduce you into taking my course, Practical Data Science for Operations Management. 
 Let me begin by reminding you that I'll be very happy to answer any questions you have. Just send them to me through an email. I sincerely believe it's much more interesting for me to answer your doubts than to simply provide a generic course description. Nevertheless, you can find some info below to give you some starting details and hopefully spark your curiosity.
 ## About the Course
 Imagine you are the Chief Operations Officer of Beanie Limited, a coffee roaster and dealer. Your company operates in Europe. Your supply-chain looks roughly like this:
 	- You import coffee beans from Brasil and Colombia. Your two procurement managers send the raw beans by ship from those locations to different docks in Europe.
 	- The coffee beans land in your European Distribution Centers. Some of them will be sent to several of your Regional Distribution Centers, and some of them will be sent to your Roasting Facilities. 
 	- In your Roasting Facilities, the beans will be roasted and blended into different possible consumer facing mixes and sent again to Distribution Centers.
 	- All of your Distribution Centers serve orders of your customers, which include retailers, horeca chains and other smaller regional coffee roasters. Different types of products are served (wholesale raw coffee beans, wholesale roasted coffee beans, different brands of retail coffee SKUs, etc).
 With all of this footprint, there is *a lot* to manage:
 - How do you transport the goods between the different levels of your network? When, how much, and to where do you send your goods?
 - What should be your stocking policy at each layer of the network? And at each node? Should they be managed independently or should you have a common strategy?
 - How will your organize your limited Roasting infrastructure? Today, should you roast coffee A or coffee B? How much of it?
 - Should you open or close locations in your network? What impact would that have on cost and performance of your supply chain?
 This course is a practical trip throughout solving this kind of problems in realistic contexts. I like to think your theoretical-oriented courses in Mathematics, Statistics, Operations have been your driving theory classes and this course is going to be your first actual driving practice. Once you graduate, you will get to actually drive on the road on your own.
 The course is mainly driven by a series of cases where several operations challenges will be presented to you. You will act like consulting teams that need to address them, with both business and technical lenses on. The lectures bring you the theory and methodologies you need to solve them.
 The course is dramatically practical, as the title implies. We will simulate consulting scenarios in real companies. You will be tasked with realistic challenges and will have to report back to simulated personas with your results. You should expect practical work every week. It's a course where you learn mostly by solving problems and a bit by listening to me. The course is also quite technical. That means a lot of new stuff related to coding, and probably a bit of refreshing your statistics courses. We will have a mini-course in the second semester for anyone that has never coded to get up to level. You should expect a lot of Python coding, which is great because it's a skill that you will find very helpful out there. For the actual contents: we will cover interesting topics such as Machine Learning, Optimization, Metaheuristics or Simulation. From a functional point of view, we will use these techniques in contexts such as demand forecasting, inventory planning, production scheduling or last-mile logistics optimization. 
 You can also expect a lot of tips and stories about how doing all of this in industry is like. I can also provide your first hand information about the job market for this field, as well as a few ideas on potential companies to land at in Barcelona.
 ## About myself
 If I'm supposed to teach you, I think you have all the right to know what my credentials are.
 I'm an adjunct professor here at UPF. That means I'm not in an academic path, but rather I work in industry, and I only provide courses in UPF in my area of specialization. This affects a lot my teaching method, and you will probably notice I do things a bit differently than most teachers. An example is taking a practical first approach.
 Currently, I work at a startup called Lola Market as a Data Engineer, where I build infrastructure to create data-driven products within the company. Our business is delivering groceries to your home, and my biggest fetiche is field operations management where we try to make our shoppers (the people who do the shopping and driving for you) as efficient as possible. Before Lola, I have held a variety of positions in consulting, research and private companies. A couple of important highlights are Accenture, where I was a Data Scientist specialized in Supply Chain and Operations, and the City of Amsterdam, where I performed Data Science research in the area of public parking.
 As for my education, I hold a MSc in Data Science from the University of Amsterdam, and a Bachelor's Degree in Business & IT from the Autonomous University of Barcelona. I also did course work in the Technical University of Munich during my Bachelor's.
 Additionally, I have several others areas of interest. These include:
 - Open source software. You can check my [Github repository](https://github.com/pmartincalvo/).
 - Bitcoin and Austrian Economics. I have my own Bitcoin node at home.
 - Electronics and micro controllers. I'm currently trying to build an automated smart watering system for my plants at home.
 Finally, you can find how students have evaluated me in my last course in [this file](![[qr_github_upf_evaluation.png]]). These scores are usually kept private by the teachers but I'm more than happy sharing them with you so you can transparently know how other students have judged me.
 Looking forward to your questions. Thanks and best regards,
 Pablo
 Now, that was my brief introduction. I expect you, being an curious and smart bunch, to have plenty of questions. Who wants to start?
 Thanks for coming. Please, don't hesitate to get in touch if more questions comes to your mind later and I hope to see at the course.
 ---
 # Agenda for the presentation
 My agenda above has been completely nuked by the fact that I will only have 5 minutes to present the course. Here is my hypercompressed version that I will do with 
 - Hi guys. 5 minutes is very little time to explain a course properly, so I won't do that. 
 - Instead, please begin by opening your phones and getting ready to read a QR code.
 - Instead, and first of all, this is my email. Please, if you have more curiosity about this course, just send me an email saying hi and I'll forward you a written text that explains more to help you decided.
 - About the course:
 	- The course is dramatically practical, as the title implies. We will simulate consulting scenarios in real companies. You will be tasked with realistic challenges and will have to report back to simulated personas with your results. You should expect practical work every week. It's a course where you learn mostly by solving problems and a bit by listening to me.
 	- The code is partly technical. That means a lot of new stuff coding, a bit of refreshing your statistics courses. We will have a mini course in the second semester for anyone that has never coded to get up to date. You should expect a lot of Python coding, which is great because it's a skill that you will find very helpful out there.
 	- For the actual contents: we will cover interesting topics such as Machine Learning, Optimization, Metaheuristics or Simulation. From a functional point of view, we will use these techniques in contexts such as demand forecasting, inventory planning, production scheduling or last-mile logistics optimization. 
 - Nevertheless, since I still have 4 minutes, let me explain you a few highlights on the course:
 - About me in 5 cents:
 	- I'm Pablo. I'm an adjunct professor. For those not familiar with the naming, that means that my main job is not to be a professor. I work in industry in private companies. The stuff I teach you in class is stuff I do in my job for a living. I currently work as a Data Engineer in a startup called Lola Market. Previously, I was Data Scientist specialized in the area of Supply Chain and Manufacturing at Accenture. I hold a MSc in Data Science from the University and a Bache
 - And we are out of time. Again, if you have any curiosity at all about the course, please just shoot me an email saying hi so I can send you my little text. You will make my day. Thanks for your time, good luck with your ongoing courses and see you soon.
 ---
 # QR codes
 Email ![[qr_upf_email.png]]
 My last course evaluation ![[qr_github_upf_evaluation.png]]
--- a/other/aot_syllabus.md
+++ b/other/aot_syllabus.md
@ -0,0 +1,263 @@
 # Applied Optimization Techniques
 PENDING REVIEW FOR 22/23 EDITION
 ## Course goals
 The goal of this course is to provide an introduction to simulation,
 optimization and machine learning techniques to students with a background in
 social sciences, with an approach biased towards practical work. The expected
 outcome is that students that have passed this course know a variety of modern
 and useful techniques that can be applied in real-life business contexts. With
 this knowledge and experience, the students understand what are the right
 techniques for different problems, which are the main steps and requirements to
 apply each of these techniques and how to judge the successful application of
 them.
 Many of the techniques taught in this course are usually taught to engineering
 and technical profiles. This course does not aim to bring students to the same
 level of technical expertise as their engineering counterparts, but rather to
 provide enough background so that the students can successfully interact with
 such profiles. Having said that, this course can also be a first introduction
 for students that are willing to pursue a more thorough learning of the
 techniques discussed in the course, after or during itself.
 With the knowledge and skills obtained in this course, students become fit for
 tasks such as:
 - Applying simulation, optimization and machine learning techniques to simple
  cases.
 - Planning and designing simulation, optimization and machine learning
  initiatives.
 - Leading simulation, optimization and machine learning projects from a
  managerial point of view.
 - Acting as a liaison between management and technical profiles in business
  contexts.
 ## Pre-requisities
 The course assumes the student has covered Mathematics I, II, III courses and
 the Probability & Statistics course. Passing this course is not impossible if
 that is not the case, but the student should expect a non-trivial challenge
 ahead.
 Knowledge of the following topics will help students better leverage this
 course, but is not strictly required:
 - Basic programming, specially in data oriented languages such as Python or R.
 - Operations research
 ## Teaching method and contents
 The course will have lecture classes and practical seminars. Classes start on
 April 7th.
 There will be 20 lecture classes and 6 practical seminars. Lecture classes will
 be used to present material to students as well as having discussions on the
 course contents. For the practical seminars, students will be divided into two
 groups with independent sessions to reduce the class size. The practical
 seminars will be used to deep-dive in the three mandatory case assignments that
 students will do throughout the course. The sesions will also be hands-on and
 students will work in the case together with the professor.
 Students are expected to attend all the activities in the course. Beyond
 lectures and practical seminars, additional reading resources will be provided
 to students. For students that need to level up their Python skills, self-paced
 materials will be suggested.
 Lectures will have the following contents:
 | Week | Classes                                                                                                                                                                | Student work                                                                                                                                                                                                                                                                                                |
 |------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | 1    | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies                                                  | - Python prep                                                                                                                                                                                                                                                                                               |
 | 2    | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation<br/> - L4: Simulation examples in Python. Introduction to case 1.                 | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1.                                           |
 | 3    | - L5: Simulation methodology. <br/> - L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here<br/> - S1: Workshop for case 1 | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics)                                                                                                              |
 | 4    | - L7: Introduction to optimization<br/> - L8: Modeling optimization problems<br/> - S2: Workshop for case 1                                                            | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) |
 | 5    | - L9: Taxonomy of optimization techniques <br/> - L10: Simulation-based optimization II. Introduction to case 2                                                        | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html)  |
 | 6    | - L11: Challenges in real-world usage. Simulation vs Optimization <br/> - L12: Introduction to Machine Learning <br/> - S3: Workshop for case 2                        | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf)                                                                                                                                                                       |
 | 7    | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2                                 | - Work on case 2 <br/> - Read case 3                                                                                                                                                                                                                                                                        |
 | 8    | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3                                              | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ)                                                                                                            |
 | 9    | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3                                   | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0)                                                                                                                                                                                        |
 | 10   | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation                                                              | - Work on case 3                                                                                                                                                                                                                                                                                            |
 | 11   | - Exam                                                                                                                                                                 | - Deliver case 3                                                                                                                                                                                                                                                                                            |                                                                                                                                                         |                                                                                                                                                                                                                                                                                                         |
 - Lecture 1 INTRO
    - Introduction to the course
        - Citizenship rules
            - Won't force you to come, but I advice you to.
            - I'll always try to start 5min late, finish 5min late, and stop
              for 5min.
            - You can come and go, just please be respectful.
        - Calendar
        - Contents
        - Expectations
        - The teacher
        - Evaluation
        - Contact
        - Questions?
        - The relevance of math and computers in management
            - Examples: pricing, logistics, staffing.
            - The skills and profiles required
            - The tools used
 - Lecture 2 INTRO
    - The techniques we will see in the course
        - Simulation
        - Optimization
        - Supervised machine learning (aka "prediction")
    - Why this stuff is important
 - Lecture 3 SIM
    - A humbling example
    - What is simulation and when do we use it
    - Different types of simulations
 - Lecture 4 SIM
    - Toy simulations in Python
    - How to approach simulation in practical terms
    - Tools in industry
 - Lecture 5 SIM
    - Theoretical background on simulation
    - Present case 1
 - Lecture 6 SIM
    - Simulation-based optimization
    - Where to go from here
 - Lecture 7 OPT
    - What is optimization
    - A trivial example
 - Lecture 8 OPT
    - Different optimization techniques
    - Present case 3
 - Lecture 9 OPT
    - How to model optimization problems (target functions, decision variables
      and constraints)
 - Lecture 10 OPT
    - Simulation-based optimization: Genetic algorithms
 - Lecture 11 OPT
    - Real world challenges and optimization deployment
 - Lecture 12 ML
    - Good news, you already know Machine Learning
    - Different branches of Machine Learning
    - Real world examples of applications
 - Lecture 13 ML
    - How does Supervised Machine Learning work?
    - Present case 2
 - Lecture 14 ML
    - The Machine Learning workflow (EDA, Feature Engineering, Model
      Evaluation, Deployment)
 - Lecture 15 ML
    - Feature Engineering
 - Lecture 16 ML
    - Model evaluation
 - Lecture 17 ML
    - Deployment and real world challenges
 - Lecture 18 Real life stories from the trenches
 - Lecture 19 Real life stories from the trenches
 - Lecture 20
    - Q&A pre-exam
    - Feedback on the course
 ## Case details
 Case 1
 - Title: Choosing stock policies under uncertainty
 - Description: Students role-play their participation as consultants in a
  project for Beanie Limited, a coffee beans roasting company. Elisa, the
  regional manager for the italian region, is not happy with their inventory
  policies for raw beans. The students are asked to analyse the problems posed
  by Elisa and apply simulation techniques, together with real data, to
  recommend a stock policy for the company's warehouse in the italian region.
  Python notebooks with some helpful prepared functions are provided to the
  students. The final delivery is a report with their recommendation to the
  client company, along with the used code.
 Case 2 candidate
 - Title: ?
 - Description: ?
 - Sample idea: https://www.gurobi.com/resource/facility-location-problem/
 Case 3
 - Title: Improving last-mile scheduling with Machine Learning
 - Description: Students role-play their participation as consultants in a
  project for Beanie Limited, a coffee beans roasting company. Pieter, the
  director of secondary transportation, has requested help from the student
  consultants. One of the key activities in Pieter's team is the daily
  scheduling, where the different trucks get assigned which deliveries and
  routes will perform. The students are asked to develop a machine-learning
  algorithm to predict the drop-time for each delivery (the drop-time is the
  time a driver takes in unloading the goods in a a client location. More
  informally, the time that passes since he removes the key from the truck
  until he starts the engine again). The goal is to provide more advanced
  information for Pieter's schedulers so they can better plan the routes of
  their drivers. The students are asked to build and deliver a Machine Learning
  algorithm that predicts this time. The students will be provided a labelled
  dataset. The final delivery is the working prediction model, along with a
  report explaining their methodology in building it, and answering some
  business questions to the client company.
 ## Grading
 The following items compose the final grade:
 - Case assignments: 50% of the grade. There will be three assignments, each
  with the same weight. The average grade of the assignments must be of 5 or
  more to pass the course.
 - Final exam: 50% of the grade. There will be a final exam at the end of the
  course. The grade must be of 5 or more to pass the course.
 Students who fail the final exam will get the chance to sit a retake exam.
 A final grade is calculated as:
 <!-- @formatter:off -->
 ```python
 if avg(case1_grade, case2_grade, case3_grade) < 5:
    passed_course = False
 if final_exam_grade < 5: 
    passed course = False
 passed_course = True
 final_grade = (avg(case1_grade, case2_grade, case3_grade) + final_exam_grade) / 2
 ```
 <!-- @formatter:on -->
 ## Bibliography
 All compulsory and required materials will be provided during the course. These
 include lecture notes, required readings and description readings.
 A good book that follows the approach of this course is "Guttag, John.
 Introduction to Computation and Programming Using Python: With Application to
 Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624", used in the
 homonymous course at MIT. It is not compulsory to use this book, but some
 students might find it helpful.
 Additional specific readings will be provided throughout the course. Students
 will be requested to read some of these materials in advance of some sessions.
 For students that want to dive deeper in the topics covered in the course, the
 following books are recommended:
 - On simulation: Louis G. Birta Gilbert Arbez, Modelling and Simulation.
  Springer 2019 ISBN: 978-3-030-18869-6 or Law A., Kelton D., Simulation and
  Modelling Analysis, Second Edition, McGraw-Hill, ISBN: 978-0071165372
 - On machine learning: Hastie T., Tibshirani R., Friedman J., The Elements Of
  Statistical Learning: Data Mining, Inference, And Prediction, Second Edition
  ISBN: 978-0387848570
 ## Cool ideas & notes
 - Hold a Kaggle competition with the students. Winners come to spend a morning
  in Accenture.
 - Start every lecture with a fun fact.
 - Let them choose the challenge for one of the practical labs.
 - Should I have office hours?
 - Will the classes be recorded?
 - What's are the policies on:
    - Late deliveries
    - Not attending exam
    - Re-takes
 - https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016
--- a/other/pds_syllabus.md
+++ b/other/pds_syllabus.md
@ -0,0 +1,254 @@
 # Practical Data Science for Operations Management
 ## Course description
 Operations Management is a field filled with opportunities to apply Data
 Science techniques. This course presents a set of practical cases where
 students can put their knowledge to use in realistic challenges and relate
 their theoretical knowledge with its application in industry. The expected
 outcome is that students that have passed this course are familiar a variety of
 modern and useful techniques that can be applied in real-life operations
 contexts. With this knowledge and experience, the students understand what are
 the right techniques for different problems, which are the main steps and
 requirements to put each of these techniques to use and how to judge their
 successful application.
 Many of the techniques taught in this course are usually taught to engineering
 and technical profiles. This course does not aim to bring students to the same
 level of technical expertise as their engineering counterparts, but rather to
 provide enough background so that the students can successfully interact with
 such profiles. Having said that, this course can also be a first introduction
 for students that are willing to pursue a more thorough learning of the
 techniques discussed in the course, after or during itself.
 ## Objectives
 With the knowledge and skills obtained in this course, students become fit for
 tasks such as:
 - Applying Data Science techniques, including optimization, simulation and
  machine learning, to simple cases at a hands-on level.
 - Planning and designing optimization, simulation and machine learning
  initiatives to solve operational challenges.
 - Leading optimization, simulation and machine learning projects from a
  managerial point of view.
 - Acting as a liaison between management and technical profiles in business
  contexts.
 - Becoming familiar with the Python programming language and several
  specialized packages within the Data Science environment.
 ## Prerequisities
 The course assumes the student has covered economics and management oriented
 Mathematics and Statistics courses at undergraduate level.
 Additional knowledge on Operations Management, Supply Chain Management and
 Manufacturing will provide a better functional context for the cases and make
 the course more profitable.
 Previous programming experience will allow students to focus much more on the
 operations side of the course. Passing this course is not impossible if that is
 not the case, but the student should expect a non-trivial challenge ahead.
 ## Methodology
 There will be 20 sessions throughout the course. Sessions will contain a mix of
 lecturing and practical work and discussions. Students will team up to work on
 several practical cases that will be handed-in throughout the course and will
 allow students to put their knowledge into practice. Python will be the tool of
 choice for most technical work, with different specialized packages used to
 tackle different parts of the course.
 Students are expected to attend all the activities in the course. Beyond class
 sessions, additional reading resources will be provided to students. For
 students that need to level up their Python skills, self-paced materials will
 be suggested.
 Lectures will have the following contents:
 | Week | Classes                                                                                                                                                                | Student work                                                                                                                                                                                                                                                                                                |
 |------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | 1    | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies                                                  | - Python prep                                                                                                                                                                                                                                                                                               |
 | 2    | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation<br/> - L4: Simulation examples in Python. Introduction to case 1.                 | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1.                                           |
 | 3    | - L5: Simulation methodology. <br/> - L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here<br/> - S1: Workshop for case 1 | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics)                                                                                                              |
 | 4    | - L7: Introduction to optimization<br/> - L8: Modeling optimization problems<br/> - S2: Workshop for case 1                                                            | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) |
 | 5    | - L9: Taxonomy of optimization techniques <br/> - L10: Simulation-based optimization II. Introduction to case 2                                                        | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html)  |
 | 6    | - L11: Challenges in real-world usage. Simulation vs Optimization <br/> - L12: Introduction to Machine Learning <br/> - S3: Workshop for case 2                        | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf)                                                                                                                                                                       |
 | 7    | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2                                 | - Work on case 2 <br/> - Read case 3                                                                                                                                                                                                                                                                        |
 | 8    | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3                                              | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ)                                                                                                            |
 | 9    | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3                                   | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0)                                                                                                                                                                                        |
 | 10   | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation                                                              | - Work on case 3                                                                                                                                                                                                                                                                                            |
 | 11   | - Exam                                                                                                                                                                 | - Deliver case 3                                                                                                                                                                                                                                                                                            |                                                                                                                                                         |                                                                                                                                                                                                                                                                                                         |
 - Lecture 1 INTRO
    - Introduction to the course
        - Citizenship rules
            - Won't force you to come, but I advice you to.
            - I'll always try to start 5min late, finish 5min late, and stop
              for 5min.
            - You can come and go, just please be respectful.
        - Calendar
        - Contents
        - Expectations
        - The teacher
        - Evaluation
        - Contact
        - Questions?
        - The relevance of math and computers in management
            - Examples: pricing, logistics, staffing.
            - The skills and profiles required
            - The tools used
 - Lecture 2 INTRO
    - The techniques we will see in the course
        - Simulation
        - Optimization
        - Supervised machine learning (aka "prediction")
    - Why this stuff is important
 - Lecture 3 SIM
    - A humbling example
    - What is simulation and when do we use it
    - Different types of simulations
 - Lecture 4 SIM
    - Toy simulations in Python
    - How to approach simulation in practical terms
    - Tools in industry
 - Lecture 5 SIM
    - Theoretical background on simulation
    - Present case 1
 - Lecture 6 SIM
    - Simulation-based optimization
    - Where to go from here
 - Lecture 7 OPT
    - What is optimization
    - A trivial example
 - Lecture 8 OPT
    - Different optimization techniques
    - Present case 3
 - Lecture 9 OPT
    - How to model optimization problems (target functions, decision variables
      and constraints)
 - Lecture 10 OPT
    - Simulation-based optimization: Genetic algorithms
 - Lecture 11 OPT
    - Real world challenges and optimization deployment
 - Lecture 12 ML
    - Good news, you already know Machine Learning
    - Different branches of Machine Learning
    - Real world examples of applications
 - Lecture 13 ML
    - How does Supervised Machine Learning work?
    - Present case 2
 - Lecture 14 ML
    - The Machine Learning workflow (EDA, Feature Engineering, Model
      Evaluation, Deployment)
 - Lecture 15 ML
    - Feature Engineering
 - Lecture 16 ML
    - Model evaluation
 - Lecture 17 ML
    - Deployment and real world challenges
 - Lecture 18 Real life stories from the trenches
 - Lecture 19 Real life stories from the trenches
 - Lecture 20
    - Q&A pre-exam
    - Feedback on the course
 ## Contents
 The course focuses on two advanced topics within the context of Operations
 Management: Optimization and Machine Learning. The backbone of the course is a
 chain of practical cases that challenge the students to use these techniques,
 applied in the Python programming language, to solve realistic problems. Hence,
 the priority for students is to solve the cases, and lectures are not an end
 but a mean to tackle the practical side of the course.
 You can find below the contents planned for each week. The final exact contents
 may be adapted to the students previous knowledge and skills to improve their
 experience.
 | Week | Contents                                                                                        |
 |------|-------------------------------------------------------------------------------------------------|
 | 1    | - Introduction and motivation of the course<br/> - Data Science in companies                    |
 | 2    | - Introduction to case 1.<br/> - Real-world challenges with exact methods optimization          |
 | 3    | - Case 1 Workshop <br/> - Introduction to Simulation and Metaheuristics                         |
 | 4    | - Introduction to case 2.<br/> - Simulation-based Optimization                                  |
 | 5    | - Case 2 Workshop <br/> - Metaheuristics Deep Dive: Genetic Algorithms                          |
 | 6    | - Introduction to case 3.<br/> - Real-world challenges with simulation and heuristic approaches |
 | 7    | - Case 3 Workshop <br/> - Introduction to Supervised Machine Learning                           |
 | 8    | - Introduction to Case 4 <br/> - Supervised Machine Learning Methodology                        |
 | 9    | - Case 4 Workshop <br/> - Algorithm Deep Dive: Decision Trees and Random Forests                |
 | 10   | - Where to go from here: further learning and carreer advice<br/> - Final Q&A, exam preparation |
 ## Evaluation Criteria
 The following items compose the final grade:
 - Case assignments: 60% of the grade. There will be several assignments, each
  with the same weight. The average grade of the assignments must be of 4 or
  more to pass the course.
 - Final exam: 40% of the grade. There will be a final exam at the end of the
  course. The grade must be of 4 or more to pass the course.
 Students who fail the final exam will get the chance to sit a retake exam.
 There is no retake for the case assignments.
 Students are required to attend 80% of the classes. Failing to do so without
 justified reason will imply a zero grade in the participation/attendance
 evaluation item and may lead to suspension from the program.
 In case of a justified no-show to an exam, the student must inform the
 corresponding faculty member and the director(s) of the program so that they
 study the possibility of rescheduling the exam (one possibility being during
 the “Retake” period). In the meantime, the student will get an “incomplete”,
 which will be replaced by the actual grade after the final exam is taken. The
 “incomplete” will not be reflected on the student’s Academic Transcript.
 Plagiarism is to use another’s work and presenting it as one’s own without
 acknowledging the sources in the correct way. All essays, reports or projects
 handed in by a student must be original work completed by the student. By
 enrolling at any UPF BSM Master of Science and signing the “Honor Code,”
 students acknowledge that they understand the schools’ policy on plagiarism and
 certify that all course assignments will be their own work, except where
 indicated by correct referencing. Failing to do so may result in automatic
 expulsion from the program.
 ## Bibliography
 All compulsory and required materials will be provided during the course. These
 include lecture notes, required readings and description readings.
 A good book that follows the approach of this course is "Guttag, John.
 Introduction to Computation and Programming Using Python: With Application to
 Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624", used in the
 homonymous course at MIT. It is not compulsory to use this book, but some
 students might find it helpful.
 Additional specific readings will be provided throughout the course. Students
 will be requested to read some of these materials in advance of some sessions.
 For students that want to dive deeper in the topics covered in the course, the
 following books are recommended:
 - On simulation: Louis G. Birta Gilbert Arbez, Modelling and Simulation.
  Springer 2019 ISBN: 978-3-030-18869-6 or Law A., Kelton D., Simulation and
  Modelling Analysis, Second Edition, McGraw-Hill, ISBN: 978-0071165372
 - On metaheuristics: Sean Luke, 2013, Essentials of Metaheuristics, Lulu,
  second edition, available for free
  at http://cs.gmu.edu/~sean/book/metaheuristics/
 - On machine learning: Hastie T., Tibshirani R., Friedman J., The Elements Of
  Statistical Learning: Data Mining, Inference, And Prediction, Second Edition
  ISBN: 978-0387848570
 ## Professor Bio
 Pablo Martín is an adjunct professor at UPF, where he teaches applied Data
 Science with a focus on Operations and Supply Chain. He has several years of
 experience in the fields of Data Science and Data Engineering in industry, both
 in Consulting and final companies. He also has experience in research. Pablo
 holds a MSc degree in Data Science from the University of Amsterdam, and a BSc
 in Business & Technology from the Autonomous University of Barcelona. Some of
 his other interests include open-source software, Austrian Economics and
 Bitcoin. You can find out more
 at [LinkedIn](https://www.linkedin.com/in/pablomartincalvo/)
 and [github](https://github.com/pmartincalvo).
--- a/other/pictures/feedback_qr.png
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+++ b/other/pictures/qr_github_upf_evaluation.png
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--- a/pds/cases/case_1/case1_student_notebook.ipynb
+++ b/pds/cases/case_1/case1_student_notebook.ipynb
--- a/pds/cases/case_1/case_1.zip
+++ b/pds/cases/case_1/case_1.zip
--- a/pds/cases/case_1/case_1_description.docx
+++ b/pds/cases/case_1/case_1_description.docx
--- a/pds/cases/case_1/case_1_description.md
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@ -0,0 +1,196 @@
 # Case 1: Simulating Stock Policies
 - Title: Choosing stock policies under uncertainty
 - Description: Students role-play their participation as consultants in a
  project for Beanie Limited, a coffee beans roasting company. Elisa, the
  regional manager for the italian region, is not happy with their inventory
  policies for raw beans. The students are asked to analyse the problems posed
  by Elisa and apply simulation techniques, together with real data, to
  recommend a stock policy for the company's warehouse in the italian region.
  Python notebooks with some helpful prepared functions are provided to the
  students. The final delivery is a report with their recommendation to the
  client company, along with the used code.
 Stuff I want them to understand:
 - The model/hypothesis/validate
 - That in a simulation you set parameters, and you observe results
 - To write in a problem-solving manner.
 - That there are trade-offs and it's not trivial to find optimal solutions.
 Elements of the simulation:
 - Demand behavior
 - Lead time and standard deviation of provider (or providers)
 - Service level
 - Punishment for sales lost
 Observable effects of policies:
 - Mean inventory at hand
 - Service level
 - Warehousing/Capital Cost
 - Lost sales cost
 # Case 1: Choosing ordering goods under uncertainty
 You are part of an expert simulation team in SimiUPF SL. You have been assigned
 to a new project with a client company, Beanie Limited. Beanie Limited is a
 coffee roasting company and also distributes raw coffee beans through Europe
 and Middle East.
 Specifically, you will be working for Elisa Bolzano, the Director of Beanie
 Limited's warehouse located in Caserta, near Naples. Elisa is the full
 responsible for all the operations in the warehouse. She has requested the help
 of the SimiUPF team because she is worried about how certain things are managed
 in the warehouse and wants your help.
 The Caserta warehouse serves the raw coffee beans distribution business of
 Beanie Limited in southern europe and the mediterranean. The warehouse and its
 team are responsible for serving clients and also other smaller regional
 warehouses from Beanie Limited in this geography. From the warehouse point of
 view, they are usually just called "the clients". Whenever one of the clients
 needs raw beans, they arrange a transport truck that goes to the warehouse to
 pick up a certain amount of goods. Elisa's team fills up the truck with the
 requested goods, and then the clients take care of receiving that at their own
 locations. Given the size and relevance of the Caserta warehouse, activity is
 pretty much constant, with goods leaving the warehouse towards client locations
 every day.
 The Caserta warehouse itself has only one way to source coffee beans to store
 in their warehouse: requesting them to the Beanie Limited central offices in
 Diemen, near Amsterdam. Whenever Elisa's team considers that more stock is
 needed, they post a Purchase Order to the central office for a certain amount
 of beans. The central office arranges the goods and the delivery and, after a
 few days, the goods reach Caserta and are stored in the warehouse. The central
 office tries to ensure a lead time of 7 days (lead time is the time that passes
 between an order being placed and the goods reaching their destination), but
 the reality is they do what they can and this time is not always respected.
 Stock is a necessary evil (it implies a lot of cost), but Elisa's warehouse
 needs it to serve the clients in their region properly. Having too little stock
 means the clients need to wait long times to get their goods, which is risky
 for the business. On the other hand, having a lot of stock means high warehouse
 costs and financial opportunity cost (if Beanie Limited has 1 million € in
 coffee beans in a warehouse, that is 1 million € they can't invest somewhere
 else to improve their business). Thus, Elisa needs the stock to be as small as
 possible, while making sure she is not disappointing clients at the same time
 because of stockouts.
 Elisa is calling you because 2021 was a terrible year for the warehouse. The
 year was a chaotic one, and Elisa's team was not able to run operations
 smoothly. Although Elisa is not providing exact numbers, she is very well aware
 that the warehouse stock was unnecessarily high at times, and that there were
 too many periods where the warehouse was out of stock and clients had to wait
 to get their goods.
 Elisa thinks the main reason for this is the lack of a clear policy for when to
 order and how much to order from Diemen. Her team decides independently when to
 do it, and Elisa has a feeling that they are not approaching these decisions
 the right way. This means that sometimes they order when there is no need to,
 sometimes they don't order when they should, and that the amounts being ordered
 might not always be the optimal ones.
 Here is where you come in. As simulation experts, Elisa expects from you that
 you can help design an ordering policy to fix these issues. Doing this implies
 examining data from last year, building a proper simulation to examine the
 different factors being involved, and deciding when and how should Elisa's team
 order more goods from Diemen.
 Elisa expects a report where you share your findings and recommendations in a
 clear way that can help her team. Also, Elisa does not trust you blindly: you
 need to motivate the reasoning behind your recommendations. Otherwise, she will
 not feel comfortable implementing your recommendations and the bosses at
 SimiUPF will be mad at you...
 ## Detailed task definition
 - Below you will find four levels of questions. Levels 1 to 3 are compulsory.
  Level 4 is optional.
 - You need to write a report document where you answer the questions of the
  different levels. This report should be directed towards Elisa, should give
  her clear recommendations and should justify these recommendations. It's
  important for you to reflect your methodology to back your proposals.
 - Each level is worth 2 points out of a total of 10. The 2 missing points will
  grade the clarity and structure of your report and code.
 - You need to use a Python notebook to solve all levels. A helper notebook is
  provided. Please attach a notebook that shows your
  solution/proposal/analysis.
 - Include your team number, names and student IDs in all your deliverables.
 ## Data
 - You are provided with three tables that contain real data from 2021.
    - demand_events: this table shows how many beans left the Caserta warehouse
      to serve clients. There is some amount leaving every day because the
      warehouse serves many small orders from small clients, so there is always
      some order being fulfilled. The amount is measured in kilograms, and
      represents the total amount that left during that day. Individual orders
      are not relevant for this case, so we only look at daily total figures.
    - sourcing_events: this table shows the Purchase Orders that Elisa's team
      placed to Diemen. For each order, there are two dates: the date when
      Elisa's team placed the order, and the date where the beans actually
      reached the Caserta warehouse. The difference between those dates is the
      real lead time of each order. The amount is measured in kilograms.
    - stock_state: this table shows the stock at the warehouse at the end of
      each date. As you can guess, the stock for a certain date is the stock of
      the previous day, plus the goods that reached Caserta coming from Diemen,
      minus the goods that left the warehouse to serve client orders. A
      negative stock is not a challenge to the laws of physics: it means
      clients are waiting for their requested beans. If one row shows -1.000,
      it means that the warehouse is empty, and clients are awaiting a total
      amount of 1.000 kgs of beans. If next morning, a 1.000 kgs reach Caserta
      from Diemen, those will be used immediately to satisfy those awaiting
      clients, and the warehouse stock will become 0.
 ## Notebook
 A notebook with some helping code has been provided. The code contains a small
 simulation engine that can help you simulate a year of activity for the
 warehouse. The instructions on how to use the code are in the notebook itself.
 ## Levels
 - Level 1
    - Elisa wants you to measure the performance of the last year, providing
      quantitative metrics. She knows it was a bad year, but hasn't looked at
      the real data to summarize how bad it was. Remember that there is a
      trade-off: too much stock, is not desired, but running out of stock and
      making clients wait is also negative.
    - Going one step further, Elisa wants to know: what was done wrong?
 - Level 2
    - Elisa wants you to propose an ordering policy. This means, that you need
      to define a rule that, once each day, should answer the questions: should
      we place a Purchase Order to request beans from Diemen today? If yes, how
      much should we order?
    - Use simulation to present metrics on what is the expected performance
      with the policy you are proposing. Remember, you need to convince Elisa
      that this is better than what happens today.
    - As a specific constraint, Elisa explains that she wants that the
      probability of a stockout to be at most of 5% on any given day.
 - Level 3
    - Right after you finished designing your policy for level 2, Elisa called
      with some news: she has just been informed by the management in Diemen
      that a new Minimum Order Quantity (MOQ) rule will begin soon. This rule
      means that, when the Caserta warehouse places an order to request
      material from Diemen, the order should be of at least 500,000 kgs of
      beans, and not less than that.
    - Elisa wants you to take this into account. Does it affect the policy you
      proposed for level 2? If so, you need to come up with a new one that
      adapts to this rule and compare it to the previous one.
 - Level 4
    - Elisa briefly discussed with you in one meeting that there is an option
      to come to an agreement with the team in Diemen to improve the lead time
      stability. The proposal from Diemen is that, if the target lead time was
      set to something higher that the current 7 days target, providing a more
      stable delivery would be feasible.
    - The specific proposal from Diemen is: if the lead time target is changed
      to 15 days, they provide a 100% guarantee that orders will be delivered
      in exactly 15 days.
    - Elisa would love if you could take some additional time to study this
      proposal. What is better for Caserta? The current 7 days target
      lead-time, with unstable delivery times? Or a fixed, 15-day lead time?
    - The MOQ rule of level 3 still applies.
--- a/pds/cases/case_1/case_1_description.pdf
+++ b/pds/cases/case_1/case_1_description.pdf
--- a/pds/cases/case_1/demand_events.csv
+++ b/pds/cases/case_1/demand_events.csv
@ -0,0 +1,366 @@
 date,demand_quantity
 2021-01-01,54609.49281314914
 2021-01-02,36208.63648649295
 2021-01-03,77784.17276763407
 2021-01-04,76481.81360421646
 2021-01-05,52305.87658918292
 2021-01-06,57098.56436860317
 2021-01-07,41565.68706138541
 2021-01-08,81995.500619844
 2021-01-09,71041.91466404148
 2021-01-10,31787.17080818402
 2021-01-11,32735.09633866546
 2021-01-12,32855.44553254065
 2021-01-13,55420.934082626205
 2021-01-14,48883.311263507494
 2021-01-15,48368.597773147136
 2021-01-16,40225.99478591274
 2021-01-17,69003.66723779934
 2021-01-18,67378.93368511106
 2021-01-19,59444.432628854185
 2021-01-20,54441.80415596864
 2021-01-21,52796.814721541414
 2021-01-22,30193.150803735854
 2021-01-23,62328.53756562836
 2021-01-24,43690.320158519615
 2021-01-25,78451.89473980921
 2021-01-26,47794.13927746792
 2021-01-27,51454.93947489077
 2021-01-28,64633.17690683539
 2021-01-29,67371.66310250101
 2021-01-30,51137.068372905895
 2021-01-31,62192.931782584405
 2021-02-01,62381.245234820446
 2021-02-02,62744.03145531537
 2021-02-03,51305.706023572566
 2021-02-04,52618.66719247759
 2021-02-05,51961.10865929137
 2021-02-06,55154.27434352692
 2021-02-07,39799.62917632264
 2021-02-08,65486.97890826721
 2021-02-09,55355.23228947571
 2021-02-10,46211.4777291026
 2021-02-11,53132.95392507133
 2021-02-12,31537.03525349067
 2021-02-13,46447.72089889987
 2021-02-14,63731.03176553111
 2021-02-15,54454.77009849779
 2021-02-16,40659.50720269109
 2021-02-17,63493.99813149876
 2021-02-18,54931.266644895266
 2021-02-19,28278.734877540137
 2021-02-20,36379.638867181835
 2021-02-21,62202.758260545044
 2021-02-22,54523.210135004185
 2021-02-23,42395.85236943305
 2021-02-24,42934.425415725156
 2021-02-25,51494.77047631462
 2021-02-26,44220.2960470736
 2021-02-27,45670.120416197926
 2021-02-28,57107.49381367681
 2021-03-01,28972.234058115788
 2021-03-02,43048.734607813065
 2021-03-03,41505.53405595842
 2021-03-04,47926.03548243223
 2021-03-05,61278.99549030161
 2021-03-06,39044.50052424295
 2021-03-07,37142.63665375576
 2021-03-08,59385.01021647509
 2021-03-09,19622.861200135892
 2021-03-10,42875.82033258566
 2021-03-11,37298.094228973925
 2021-03-12,53411.899019061944
 2021-03-13,45345.99865109816
 2021-03-14,53211.40616195306
 2021-03-15,40974.40081655905
 2021-03-16,56025.67583148412
 2021-03-17,42957.88421097572
 2021-03-18,65464.99283743926
 2021-03-19,28628.77720679815
 2021-03-20,50873.13077669
 2021-03-21,47215.115350042746
 2021-03-22,44982.481462385775
 2021-03-23,72845.44784612038
 2021-03-24,36657.28355561715
 2021-03-25,35932.62440127316
 2021-03-26,90802.53749884429
 2021-03-27,54150.36198995029
 2021-03-28,57725.715294590715
 2021-03-29,49797.541628930994
 2021-03-30,39842.574327318325
 2021-03-31,46648.05822011224
 2021-04-01,38251.20061495644
 2021-04-02,54968.95147105346
 2021-04-03,39223.33668121346
 2021-04-04,55196.72314245463
 2021-04-05,60193.96623402014
 2021-04-06,107790.97235982082
 2021-04-07,58927.3553815537
 2021-04-08,52570.524217849554
 2021-04-09,40996.74684261808
 2021-04-10,52952.91853803685
 2021-04-11,55117.279622249654
 2021-04-12,62330.90239991735
 2021-04-13,60352.159875666686
 2021-04-14,46481.1929993728
 2021-04-15,26740.04853400801
 2021-04-16,58824.75809726865
 2021-04-17,48919.848176294996
 2021-04-18,69164.97343682637
 2021-04-19,65052.99346838036
 2021-04-20,86948.6316872793
 2021-04-21,47600.922050548594
 2021-04-22,53629.43407349051
 2021-04-23,58802.85640700405
 2021-04-24,27277.291629712032
 2021-04-25,72991.08369503866
 2021-04-26,46319.17825995694
 2021-04-27,42473.64434623195
 2021-04-28,62986.327912551824
 2021-04-29,59770.868769586974
 2021-04-30,46967.110213491585
 2021-05-01,31283.252270527257
 2021-05-02,45270.96133039481
 2021-05-03,40200.06151139432
 2021-05-04,28769.438869243786
 2021-05-05,41597.284397045456
 2021-05-06,42053.59694349442
 2021-05-07,32056.90063878994
 2021-05-08,24126.23251230451
 2021-05-09,61874.94040944404
 2021-05-10,69582.18210731493
 2021-05-11,54713.709988929106
 2021-05-12,77986.61766717135
 2021-05-13,51047.031274850284
 2021-05-14,59715.32807151039
 2021-05-15,73688.19223261088
 2021-05-16,65807.03078052355
 2021-05-17,55779.76069593255
 2021-05-18,53655.30817237868
 2021-05-19,53863.25586084147
 2021-05-20,42447.86518825701
 2021-05-21,34634.185379985654
 2021-05-22,81385.8091352819
 2021-05-23,53674.49856663084
 2021-05-24,32130.44754196027
 2021-05-25,35141.955123039676
 2021-05-26,35379.77494659018
 2021-05-27,47723.22357446625
 2021-05-28,50903.4531491154
 2021-05-29,52597.71388776773
 2021-05-30,37762.845725518426
 2021-05-31,31687.345250434668
 2021-06-01,1380.9898989639114
 2021-06-02,39384.958015718286
 2021-06-03,62407.74873554036
 2021-06-04,65856.83339328374
 2021-06-05,41159.52864583683
 2021-06-06,78292.78851815795
 2021-06-07,45085.067801033474
 2021-06-08,63969.20178674298
 2021-06-09,61865.47920564571
 2021-06-10,66481.65277980786
 2021-06-11,51663.83884564799
 2021-06-12,49462.60941335073
 2021-06-13,48907.56631014691
 2021-06-14,27822.17014448859
 2021-06-15,35277.37023428074
 2021-06-16,58563.35766039751
 2021-06-17,62202.644540009576
 2021-06-18,59361.79725578232
 2021-06-19,37617.54204811233
 2021-06-20,84719.87850010264
 2021-06-21,50153.4959152938
 2021-06-22,40323.203680923136
 2021-06-23,34962.05953043287
 2021-06-24,46613.355492701965
 2021-06-25,53246.878843729624
 2021-06-26,38693.95753463766
 2021-06-27,64529.674857993334
 2021-06-28,41126.42916746255
 2021-06-29,71984.7315338233
 2021-06-30,28888.043384351673
 2021-07-01,60293.9028556177
 2021-07-02,49816.29840729628
 2021-07-03,42812.38643232065
 2021-07-04,34909.73927750447
 2021-07-05,37591.53584671515
 2021-07-06,39469.203591839716
 2021-07-07,54396.08709948022
 2021-07-08,28047.275778018222
 2021-07-09,41834.25913212226
 2021-07-10,42604.985980117504
 2021-07-11,57706.78926368313
 2021-07-12,37965.84096167571
 2021-07-13,42719.54678256345
 2021-07-14,36541.18442771253
 2021-07-15,45179.2123752051
 2021-07-16,49107.119659072996
 2021-07-17,28547.87933059051
 2021-07-18,49714.75688145967
 2021-07-19,33936.612529083315
 2021-07-20,72130.34325424329
 2021-07-21,21218.431770514377
 2021-07-22,47114.58552828316
 2021-07-23,62864.89434803029
 2021-07-24,48265.27576417639
 2021-07-25,82856.83438714968
 2021-07-26,71619.09933599173
 2021-07-27,31313.91831932018
 2021-07-28,51702.76017876872
 2021-07-29,51759.91074963173
 2021-07-30,43399.332699545244
 2021-07-31,55424.54037571451
 2021-08-01,42225.94672589529
 2021-08-02,34807.53319498364
 2021-08-03,73469.6548372101
 2021-08-04,27209.4505106898
 2021-08-05,57237.08622864778
 2021-08-06,45169.07725691487
 2021-08-07,51028.44462209041
 2021-08-08,40990.419651217926
 2021-08-09,31432.767517597265
 2021-08-10,46704.92168243732
 2021-08-11,67447.45628232439
 2021-08-12,56060.762852218075
 2021-08-13,36096.04292632875
 2021-08-14,82159.16133987988
 2021-08-15,61384.53830739901
 2021-08-16,42921.0220131585
 2021-08-17,57574.80918470686
 2021-08-18,44223.76579375525
 2021-08-19,38112.188923509486
 2021-08-20,43302.275718994686
 2021-08-21,31995.553894163357
 2021-08-22,51456.16324022061
 2021-08-23,51376.41164803253
 2021-08-24,45624.593753100846
 2021-08-25,46734.78195159169
 2021-08-26,37565.07483616892
 2021-08-27,23554.39766955899
 2021-08-28,20604.948141803365
 2021-08-29,66334.25895451049
 2021-08-30,48047.85418448473
 2021-08-31,56191.71390404747
 2021-09-01,44859.282252098456
 2021-09-02,46030.147501430656
 2021-09-03,10703.823438653832
 2021-09-04,70343.60042856235
 2021-09-05,69607.14131423642
 2021-09-06,56931.55211394906
 2021-09-07,54663.61348397007
 2021-09-08,57725.529008129895
 2021-09-09,53757.39275518815
 2021-09-10,53480.74906036454
 2021-09-11,49602.291868261746
 2021-09-12,52985.895433602054
 2021-09-13,37411.73715166042
 2021-09-14,59155.55398150197
 2021-09-15,21991.02211112378
 2021-09-16,59508.78533477017
 2021-09-17,41544.13170794038
 2021-09-18,46878.16624464087
 2021-09-19,32469.82943570702
 2021-09-20,61806.26905613678
 2021-09-21,56074.725664414334
 2021-09-22,53483.80545741505
 2021-09-23,30077.209266523543
 2021-09-24,73070.54849698953
 2021-09-25,44118.377703017635
 2021-09-26,34134.3360656615
 2021-09-27,61619.510801440054
 2021-09-28,29334.959480643633
 2021-09-29,54840.778405071345
 2021-09-30,54861.25954092192
 2021-10-01,37666.69406650353
 2021-10-02,45514.88974301199
 2021-10-03,37872.595956602185
 2021-10-04,54862.49528732663
 2021-10-05,72168.41067112274
 2021-10-06,58411.76789552352
 2021-10-07,64450.64193866483
 2021-10-08,39846.169995410615
 2021-10-09,51012.92307031886
 2021-10-10,56193.97181413436
 2021-10-11,34065.44429410843
 2021-10-12,58206.460717550566
 2021-10-13,46487.945645762295
 2021-10-14,50076.70184963691
 2021-10-15,47580.71432500986
 2021-10-16,53915.82908269834
 2021-10-17,39606.35607109019
 2021-10-18,73790.25224218029
 2021-10-19,38407.621781936425
 2021-10-20,33404.97538990958
 2021-10-21,59931.9601178157
 2021-10-22,74486.16955897454
 2021-10-23,50195.02837816861
 2021-10-24,40073.20302847418
 2021-10-25,64626.796001266266
 2021-10-26,61727.34307665966
 2021-10-27,62187.88733591297
 2021-10-28,54214.87801602549
 2021-10-29,67142.34221772531
 2021-10-30,46487.69937914996
 2021-10-31,31086.740684974324
 2021-11-01,48278.95337799651
 2021-11-02,20718.68300716247
 2021-11-03,58138.40065378947
 2021-11-04,49722.30296011415
 2021-11-05,36742.138456983004
 2021-11-06,42736.48890700623
 2021-11-07,55356.6885726762
 2021-11-08,81832.3429551895
 2021-11-09,62338.173681547836
 2021-11-10,39202.33687407937
 2021-11-11,62786.50002194336
 2021-11-12,37726.689751497914
 2021-11-13,67033.48460270898
 2021-11-14,55366.81040522425
 2021-11-15,53898.24191372635
 2021-11-16,62845.981914852084
 2021-11-17,33833.82833106041
 2021-11-18,61076.99869993116
 2021-11-19,51228.112090794835
 2021-11-20,55635.47027518508
 2021-11-21,71803.01115735975
 2021-11-22,50315.05762449138
 2021-11-23,60710.007411381375
 2021-11-24,73249.01607526309
 2021-11-25,57699.01149670034
 2021-11-26,46798.292724322295
 2021-11-27,36359.18817807891
 2021-11-28,61511.52093729363
 2021-11-29,39284.72872960448
 2021-11-30,28462.06773230841
 2021-12-01,25809.261932155227
 2021-12-02,59425.1826389642
 2021-12-03,43014.05369644615
 2021-12-04,52769.507877984564
 2021-12-05,56657.291422193426
 2021-12-06,59175.14433261302
 2021-12-07,57450.71229516849
 2021-12-08,25887.75148158159
 2021-12-09,21300.79633013303
 2021-12-10,49686.47609053778
 2021-12-11,50683.5775985572
 2021-12-12,43090.41843560319
 2021-12-13,28815.44447997063
 2021-12-14,31445.73933682877
 2021-12-15,50964.200286431944
 2021-12-16,37689.76522472434
 2021-12-17,39270.444361100475
 2021-12-18,63767.92920582164
 2021-12-19,66245.76864762916
 2021-12-20,64310.02645239804
 2021-12-21,20186.46628098661
 2021-12-22,57829.12348425346
 2021-12-23,61209.40407684893
 2021-12-24,60229.294569424455
 2021-12-25,57790.197713617585
 2021-12-26,59848.30412950744
 2021-12-27,48843.47435878844
 2021-12-28,45483.444566160666
 2021-12-29,45361.81436223178
 2021-12-30,57103.88645952773
 2021-12-31,49479.32345442135
--- a/pds/cases/case_1/grading.xlsx
+++ b/pds/cases/case_1/grading.xlsx
--- a/pds/cases/case_1/grading_team_1.xlsx
+++ b/pds/cases/case_1/grading_team_1.xlsx
--- a/pds/cases/case_1/grading_team_2.xlsx
+++ b/pds/cases/case_1/grading_team_2.xlsx
--- a/pds/cases/case_1/grading_team_3.xlsx
+++ b/pds/cases/case_1/grading_team_3.xlsx
--- a/pds/cases/case_1/grading_team_4.xlsx
+++ b/pds/cases/case_1/grading_team_4.xlsx
--- a/pds/cases/case_1/grading_team_5.xlsx
+++ b/pds/cases/case_1/grading_team_5.xlsx
--- a/pds/cases/case_1/sourcing_events.csv
+++ b/pds/cases/case_1/sourcing_events.csv
@ -0,0 +1,61 @@
 request_date,delivery_date,amount
 2021-06-18,2021-06-24,361622.08421162824
 2021-04-08,2021-04-17,404943.20818378055
 2021-08-02,2021-08-10,372079.3749313439
 2021-03-23,2021-03-28,324410.8683704191
 2021-07-14,2021-07-19,467167.83305448893
 2021-03-02,2021-03-09,280731.9688885999
 2021-07-18,2021-07-26,369123.2301230304
 2021-02-18,2021-02-27,384645.34262920194
 2021-01-21,2021-01-25,310407.1921732673
 2021-08-12,2021-08-21,366174.22830054327
 2021-11-02,2021-11-09,391623.2685507731
 2021-07-13,2021-07-21,345458.2161390513
 2021-06-01,2021-06-10,360467.175317622
 2021-10-20,2021-10-31,336289.0502818366
 2021-07-21,2021-07-29,381876.17957110034
 2021-10-06,2021-10-11,314261.8504829489
 2021-11-15,2021-11-22,308806.6478588278
 2021-09-05,2021-09-14,330698.694265319
 2021-12-26,2022-01-04,407311.3676167535
 2021-01-16,2021-01-25,214247.55937424488
 2021-04-20,2021-04-26,269445.2778637154
 2021-01-22,2021-01-27,479246.85314207803
 2021-02-02,2021-02-11,347470.77377278876
 2021-11-26,2021-12-01,302533.4097257286
 2021-08-23,2021-09-01,381700.4232063111
 2021-03-31,2021-04-09,277635.5674830633
 2021-06-03,2021-06-10,190836.06099482052
 2021-06-30,2021-07-07,352714.6964695578
 2021-08-10,2021-08-18,281208.5043011291
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--- a/pds/cases/case_1/stock_state.csv
+++ b/pds/cases/case_1/stock_state.csv
@ -0,0 +1,366 @@
 date,amount_in_stock
 2021-01-01,647479.2516513831
 2021-01-02,611270.6151648902
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--- a/pds/cases/case_1/summary_first_sweep.csv
+++ b/pds/cases/case_1/summary_first_sweep.csv
@ -0,0 +1,10 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
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--- a/pds/cases/case_1/summary_second_sweep.csv
+++ b/pds/cases/case_1/summary_second_sweep.csv
@ -0,0 +1,26 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
 0.8,1.0,0.5629041095890411,0.5630136986301371,0.25173917949278124,100,-17208.65752949769,31032.43630715287,200730.55535889775,100,49.05,49.0,1.166666666666668,100
 0.8,1.05,0.804986301369863,0.8452054794520547,0.14385248604678155,100,155614.19545368513,176960.00812669177,89250.77498164216,100,47.39,47.0,0.7900262110470866,100
 0.8,1.1,0.8659726027397261,0.8876712328767123,0.09160908657556802,100,198680.3804320387,207071.20423920936,54616.27896318188,100,45.45,45.0,0.7017294652672376,100
 0.8,1.15,0.924082191780822,0.9342465753424658,0.042773373946112477,100,240782.04918482568,243459.64652624974,29223.097332241305,100,43.46,43.5,0.7305733185227711,100
 0.8,1.2,0.9414794520547946,0.9493150684931506,0.03451593530365504,100,261676.29869938738,264285.581551831,25131.022316170343,100,41.83,42.0,0.6971080231639825,100
 0.9,1.0,0.6648493150684932,0.6945205479452055,0.21570939170830844,100,73024.72198996953,104935.78104756039,163647.97407003478,100,49.32,49.0,0.8862587350511948,100
 0.9,1.05,0.8186575342465754,0.8753424657534246,0.16907143140798406,100,182114.7427397742,214236.05435841338,108978.10420990313,100,47.46,47.0,0.9147500624584167,100
 0.9,1.1,0.9226849315068493,0.9397260273972603,0.06860053019119008,100,257549.83941332222,264802.2368256551,46594.05059506942,100,45.65,46.0,0.7833494518006421,100
 0.9,1.15,0.9611506849315069,0.9726027397260274,0.040083238669095175,100,297073.0555063267,302993.9769790715,33827.59431750586,100,43.63,44.0,0.7057484028184599,100
 0.9,1.2,0.9742739726027397,0.9808219178082191,0.020594090521446923,100,316243.2068669456,319849.0246099904,23997.41545045925,100,42.05,42.0,0.6871842709362761,100
 1.0,1.0,0.6921643835616439,0.7698630136986302,0.2448481301495943,100,101882.22346645949,160026.78900577017,180014.0847194441,100,49.41,49.0,1.0739806066379143,100
 1.0,1.05,0.8867945205479453,0.936986301369863,0.1266759950750295,100,246218.68552190147,273469.8906422792,85390.22913704212,100,47.72,48.0,0.7923880286064316,100
 1.0,1.1,0.9432054794520548,0.9698630136986301,0.07141874718689835,100,301924.2700638766,316299.9013534653,59963.75704883238,100,45.76,46.0,0.7123726184201268,100
 1.0,1.15,0.9764109589041096,0.9863013698630136,0.02872101571594946,100,340290.7186400273,343600.27244723897,34849.39399657999,100,43.88,44.0,0.7286350876186868,100
 1.0,1.2,0.9906575342465753,0.9945205479452055,0.0113001726894409,100,369036.41037286026,371895.01996879186,24001.498220542777,100,42.05,42.0,0.6256309946079575,100
 1.1,1.0,0.7282465753424657,0.7479452054794521,0.2260275046018253,100,139083.56089769275,162758.51029021217,168922.45751711642,100,49.45,49.5,1.0576799462440758,100
 1.1,1.05,0.9035068493150685,0.9671232876712329,0.16213843415295967,100,289746.11535166804,320941.33405487105,114866.00516221055,100,47.71,48.0,0.7951240294584386,100
 1.1,1.1,0.9613698630136986,0.989041095890411,0.0655565291501118,100,350031.4173621522,367119.16888596804,60281.0758667702,100,45.9,46.0,0.7719841941125448,100
 1.1,1.15,0.9912054794520548,0.9972602739726028,0.017645347308402025,100,396603.9442134802,400346.3145094304,29483.57515994371,100,44.0,44.0,0.7247430753394793,100
 1.1,1.2,0.9956164383561643,1.0,0.007052473418545561,100,417138.91165137023,418615.9689806167,23055.605401908946,100,42.1,42.0,0.6741998624632427,100
 1.2,1.0,0.7846575342465754,0.8698630136986301,0.2252867428579435,100,193695.8595228242,242047.6119066357,194075.09374543617,100,49.79,50.0,1.121822110891404,100
 1.2,1.05,0.9479178082191781,0.9780821917808219,0.09026491930718351,100,346443.9102815047,358689.5454693798,87392.79621129965,100,48.05,48.0,0.7436600722307887,100
 1.2,1.1,0.9872602739726026,0.9972602739726028,0.025089991771959474,100,411297.4581000472,416474.64406344807,44380.80426082012,100,46.0,46.0,0.7106690545187017,100
 1.2,1.15,0.9916438356164383,1.0,0.022282747753160106,100,441406.6727818038,448100.49807167007,38234.435371301166,100,44.19,44.0,0.7063206700139026,100
 1.2,1.2,0.9969589041095891,1.0,0.007179214412726819,100,468382.008571895,474174.00471737224,28487.211871335898,100,42.35,42.0,0.6723244767373897,100
--- a/pds/cases/case_1/summary_third_sweep.csv
+++ b/pds/cases/case_1/summary_third_sweep.csv
@ -0,0 +1,71 @@
 reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
 0.8,1.05,0.7859726027397261,0.8260273972602741,0.1598362225708639,100,144208.47823315178,169318.30356595665,100514.74254469664,100,47.4,47.0,0.8164965809277261,100
 0.8,1.06,0.794958904109589,0.8301369863013699,0.1403919914831539,100,153586.2551041631,173418.410864591,78290.88655784576,100,47.11,47.0,0.7371114795831993,100
 0.8,1.07,0.8067671232876712,0.8315068493150686,0.1339955549137507,100,161441.590865957,172098.93199610294,72690.59543201819,100,46.56,47.0,0.7291893935852132,100
 0.8,1.08,0.8381917808219178,0.8493150684931507,0.10243407881742274,100,180757.48462696798,185912.87023764107,63117.92391789514,100,46.47,46.0,0.7843803447642761,100
 0.8,1.09,0.8667671232876712,0.8808219178082193,0.08505385831720369,100,198896.08671044532,205127.32601251808,47198.21828806646,100,45.77,46.0,0.664466064983872,100
 0.8,1.1,0.878027397260274,0.9054794520547945,0.09679462977911307,100,207399.79024280803,225251.01385664742,55542.82909581238,100,45.52,46.0,0.7032392583932264,100
 0.8,1.11,0.8961643835616439,0.9123287671232877,0.06811908370060707,100,218048.13332393923,226635.2932288471,41083.817034265245,100,45.0,45.0,0.7654139963827331,100
 0.8,1.12,0.9054246575342466,0.9178082191780822,0.05473703917338135,100,226233.79870989014,228435.7464824344,33790.75003378518,100,44.75,45.0,0.770346898114313,100
 0.8,1.13,0.9113972602739726,0.9219178082191781,0.057722017942900286,100,229820.05176361586,234205.64538325084,37180.29105911812,100,44.41,44.0,0.6371495868001452,100
 0.8,1.14,0.9096164383561645,0.9260273972602739,0.06476061395141362,100,229451.0061609741,236898.16887785456,39996.703715484575,100,43.98,44.0,0.6192207550093444,100
 0.9,1.05,0.799068493150685,0.8589041095890411,0.17478733730560359,100,170856.8688515813,195706.0585029618,107559.81482477198,100,47.66,48.0,0.7278028371042323,100
 0.9,1.06,0.8728493150684932,0.9178082191780822,0.14035816656132336,100,214583.50564701803,237766.20853748685,91537.75933622228,100,47.16,47.0,0.9180567971690441,100
 0.9,1.07,0.8863835616438357,0.9246575342465753,0.13202268131162476,100,225962.8131575521,248956.4255873827,87091.59551249178,100,46.85,47.0,0.7703468981143126,100
 0.9,1.08,0.8878356164383561,0.9109589041095891,0.1034915126220675,100,231793.74733914566,241230.59100630687,65364.238707012075,100,46.37,46.0,0.7608474807008903,100
 0.9,1.09,0.910958904109589,0.9342465753424658,0.0848895391251729,100,247414.19854095648,259646.62831328082,56859.363211731616,100,46.14,46.0,0.829019135649309,100
 0.9,1.1,0.9097808219178083,0.936986301369863,0.09263505453625975,100,248051.95584330414,261613.48309493947,62298.32416064076,100,45.61,46.0,0.8027100562117265,100
 0.9,1.11,0.9249041095890411,0.9493150684931506,0.06777942231931827,100,259875.5154301237,265650.987470857,44837.543279018144,100,45.24,45.0,0.7123726184201262,100
 0.9,1.12,0.9426575342465753,0.958904109589041,0.054991006506983194,100,276332.38956547884,283931.61956175056,40395.41216420654,100,44.78,45.0,0.7327821620165825,100
 0.9,1.13,0.9434794520547944,0.9657534246575343,0.05636688915712585,100,277312.20724561514,285598.0109710443,42114.061861274546,100,44.54,44.0,0.6422812876966388,100
 0.9,1.14,0.9461369863013698,0.9671232876712329,0.05949943242463646,100,281586.1900728949,292772.94372766616,44568.637852341606,100,44.05,44.0,0.7436600722307894,100
 0.95,1.05,0.8504657534246576,0.8876712328767123,0.1426216962196151,100,209502.91100042395,231229.60137442837,98473.14510338863,100,47.62,48.0,0.7885544888073256,100
 0.95,1.06,0.8763561643835617,0.9205479452054794,0.13371982345233996,100,232132.86769214022,253984.80407355353,85226.62571623079,100,47.18,47.0,0.7571877794400369,100
 0.95,1.07,0.8897534246575343,0.9232876712328767,0.11000276133429772,100,244444.98080390875,258917.87933101476,70323.6063035861,100,46.75,47.0,0.7833494518006393,100
 0.95,1.08,0.9206301369863015,0.958904109589041,0.09976094450434589,100,265926.47402984084,283736.9000909083,66614.08688632757,100,46.37,46.0,0.812217316792107,100
 0.95,1.09,0.9294794520547945,0.958904109589041,0.08543716778698167,100,274030.97554696375,285147.23292353324,56892.76092575346,100,46.01,46.0,0.7452882297839849,100
 0.95,1.1,0.9287123287671233,0.9547945205479451,0.08003250087101145,100,274078.3527874601,283856.4572754771,56690.76457159778,100,45.64,46.0,0.6744994402884967,100
 0.95,1.11,0.9364383561643835,0.9561643835616438,0.06526268605058627,100,280881.6829739677,296677.6782650106,49506.88718738129,100,45.34,45.0,0.6391281940932124,100
 0.95,1.12,0.9556438356164384,0.973972602739726,0.07386818268433056,100,298727.4002688783,311051.6643517049,55230.95325025695,100,44.94,45.0,0.6327748733981471,100
 0.95,1.13,0.9625479452054795,0.9753424657534246,0.03853770121887277,100,303624.8432092603,305964.4471615079,33997.45186113061,100,44.56,45.0,0.6562827673397108,100
 0.95,1.14,0.965041095890411,0.9808219178082191,0.04503533313503058,100,309887.30047959444,316649.4679522059,37629.163123648126,100,44.27,44.0,0.6942039987778751,100
 1.0,1.05,0.878054794520548,0.9232876712328767,0.12456329754626162,100,239357.93264631205,263694.2998236382,80089.22781996857,100,47.79,48.0,0.8680059581790576,100
 1.0,1.06,0.8947123287671234,0.941095890410959,0.1355028323290669,100,255749.40900901722,285246.23294684856,94956.97950416044,100,47.34,47.0,0.7683118053909035,100
 1.0,1.07,0.9047945205479452,0.9438356164383561,0.13250530459875723,100,264039.6231237091,286009.00904912734,90347.19839737378,100,46.86,47.0,0.7787584005740479,100
 1.0,1.08,0.9243287671232876,0.952054794520548,0.08231970120182112,100,281449.77736003743,293460.12722831377,61529.54372494277,100,46.59,47.0,0.726065917924618,100
 1.0,1.09,0.9343013698630136,0.9643835616438357,0.08847012172074435,100,291842.87457335443,304680.33844471246,67944.88567969218,100,45.95,46.0,0.74366007223079,100
 1.0,1.1,0.9562739726027396,0.9753424657534246,0.05972417397215357,100,312685.41726779164,317915.94000983867,49197.89880376956,100,45.67,46.0,0.7792122414154682,100
 1.0,1.11,0.968958904109589,0.9808219178082191,0.03477533957582285,100,323801.7037949509,331782.3224302352,37926.45459276231,100,45.43,45.0,0.781800563578795,100
 1.0,1.12,0.9685479452054794,0.9835616438356164,0.04226908937517442,100,325205.9243724567,337201.9597184559,45250.733446382925,100,44.9,45.0,0.6890192121758836,100
 1.0,1.13,0.9677808219178082,0.9808219178082191,0.04733685887394322,100,330657.6603956923,337075.92965660174,42421.01611114322,100,44.6,45.0,0.7247430753394793,100
 1.0,1.14,0.9823013698630138,0.9917808219178083,0.02489902195487209,100,346636.5895811218,351643.50356452586,32724.192897340898,100,44.11,44.0,0.6947857747012907,100
 1.05,1.05,0.9096712328767123,0.9506849315068493,0.1268551724970295,100,275036.8994307617,290241.47330518905,92189.74314580947,100,47.76,48.0,0.8542230210530327,100
 1.05,1.06,0.9146575342465754,0.9561643835616438,0.12009700618062043,100,283383.00552277366,305048.07351176,96246.65543086598,100,47.27,47.0,0.7365631354644712,100
 1.05,1.07,0.9413972602739726,0.9698630136986301,0.09003060152571733,100,303722.93403703946,313331.6029302139,73057.08547382594,100,46.91,47.0,0.7533762390570582,100
 1.05,1.08,0.9469315068493152,0.9780821917808219,0.0793003279699756,100,317055.4136440441,329838.54958045797,63485.26929871185,100,46.59,47.0,0.7666666666666667,100
 1.05,1.09,0.9626575342465753,0.9835616438356164,0.050483002706923574,100,327783.50728198123,337811.29494201415,48868.03042293043,100,46.26,46.0,0.7603826787755086,100
 1.05,1.1,0.9624109589041097,0.9794520547945205,0.05435562429151819,100,331620.04862504784,339715.6067690905,51905.17082289278,100,45.78,46.0,0.7464393593398702,100
 1.05,1.11,0.9697534246575343,0.989041095890411,0.04971307496436453,100,340227.99533744523,351120.098823712,49956.8593361718,100,45.43,45.0,0.7420283421851632,100
 1.05,1.12,0.9702739726027397,0.9863013698630136,0.05158788657894017,100,343659.58516506077,353342.32752993377,44689.97958762292,100,45.05,45.0,0.7299508769967222,100
 1.05,1.13,0.9841643835616438,0.9917808219178083,0.021220325968317567,100,362923.71636342397,367782.8510066926,30511.492738523157,100,44.68,45.0,0.7089613971340485,100
 1.05,1.14,0.9803287671232878,0.989041095890411,0.03439019008566572,100,358818.66979266173,366030.73615185026,38779.97603060032,100,44.42,44.0,0.6693883835866954,100
 1.1,1.05,0.8774520547945205,0.9493150684931506,0.16332896109997266,100,262116.4533938322,307218.83635365096,137001.0438185493,100,47.65,48.0,0.845367650579594,100
 1.1,1.06,0.93,0.9575342465753425,0.08998299067660721,100,308184.9288191942,315262.4431269691,76531.77678714157,100,47.33,47.0,0.7114503609961922,100
 1.1,1.07,0.9332054794520548,0.9767123287671233,0.1133241844881452,100,316339.8582983121,343388.79801796563,88723.83399772066,100,47.18,47.0,0.7704124567628391,100
 1.1,1.08,0.9466849315068494,0.9767123287671233,0.09839032697603153,100,331184.26416084,349136.8427766626,81927.62325661813,100,46.52,47.0,0.8346050901867643,100
 1.1,1.09,0.9556164383561644,0.989041095890411,0.10547415073320952,100,344293.7967364825,363724.13265618257,85256.01192642892,100,46.26,46.0,0.7333333333333325,100
 1.1,1.1,0.9733150684931507,0.9945205479452055,0.06351837655542099,100,363147.94176298834,374326.82103307196,57145.65533432398,100,45.75,46.0,0.6256309946079563,100
 1.1,1.11,0.9746849315068493,0.9917808219178083,0.04402911492630584,100,363388.1927891669,373491.37867364136,49274.54501585131,100,45.5,45.0,0.6435381994422805,100
 1.1,1.12,0.9793972602739726,0.9917808219178083,0.03915141511936552,100,370890.05170730536,376338.38252097333,44302.65600471223,100,45.1,45.0,0.7035264706814484,100
 1.1,1.13,0.9804109589041096,0.9945205479452055,0.03243412017637617,100,377327.82974791917,381348.09184570936,38488.17279855358,100,44.76,45.0,0.7123726184201263,100
 1.1,1.14,0.9886849315068492,0.9945205479452055,0.017334789000443024,100,384809.10717216856,390844.7511152257,31093.991487524258,100,44.51,44.0,0.6589707309451781,100
 1.2,1.05,0.9171780821917808,0.9726027397260274,0.13067274741673798,100,322060.45665888296,340538.9200925699,105710.6766863121,100,47.92,48.0,0.8490041700769674,100
 1.2,1.06,0.9489041095890411,0.9917808219178083,0.09929085079019878,100,355803.9539068448,384192.0333909353,97019.18401447838,100,47.61,48.0,0.7771353768420244,100
 1.2,1.07,0.9564109589041097,0.9917808219178083,0.07656699649287621,100,364057.84548111144,389141.36283888033,75902.53849836365,100,47.31,47.0,0.6918720031095832,100
 1.2,1.08,0.9602465753424657,0.989041095890411,0.08505995544913891,100,373446.1741382272,386689.89061583555,83449.85958080231,100,46.81,47.0,0.6918720031095821,100
 1.2,1.09,0.9672054794520548,0.9945205479452055,0.08413140089715565,100,390837.72881569655,407689.56891719927,79174.94776761712,100,46.26,46.0,0.7194273817250479,100
 1.2,1.1,0.9738356164383561,0.9972602739726028,0.060117001280751294,100,398871.1618030684,414081.79698469874,60975.215674990715,100,46.23,46.0,0.6794977348874467,100
 1.2,1.11,0.9765205479452055,0.9972602739726028,0.05166994864627592,100,404329.81311479496,423147.71118130337,66661.79450110722,100,45.64,46.0,0.6744994402884964,100
 1.2,1.12,0.9849315068493151,0.9972602739726028,0.04728618617716756,100,424054.9128070699,431696.0971162426,53638.952963032265,100,45.25,45.0,0.7436600722307911,100
 1.2,1.13,0.991041095890411,0.9986301369863013,0.018392476408138413,100,428392.03453004366,434494.4380003613,39705.52165661862,100,44.91,45.0,0.6528105515090998,100
 1.2,1.14,0.992986301369863,1.0,0.01664151932365369,100,438627.18168542744,445798.6518808934,35926.80443305188,100,44.45,44.0,0.6871842709362769,100
--- a/pds/cases/case_2/Case
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--- a/pds/cases/case_2/Case
+++ b/pds/cases/case_2/Case
@ -0,0 +1,80 @@
 You have been hired by Beanie Limited, the coffee company, for a new project on their Diemen distribution center (DC). Your contact for this engagement is Jeroen Schotten, the manager of the facility.
 The Diemen DC is the entrypoint of Beanie Limited's supply chain in Europe. Beanie Limited purchases raw coffee beans in different regions of Latin America that are sent by ship to docks in Europe. Once the beans reach Europe, all the stock is centralized in Diemen before continuing its path through Beanie Limited's network. The Diemen DC does not serve customers directly, but rather other distribution centers of Beanie Limited and some partner companies. These regional DCs are smaller and are the ones responsible for interacting with clients directly in their assigned areas.
 As of today, Beanie Limited only handles raw coffee beans through it's sales network and supply chain. But this is going to change very soon, which is the reason Jeroen has decided to hire you.
 Beanie Limited is currently working on an expansion of their Diemen DC, with the goal of adding a processing facility for coffee. This processing facility will be capable of producing roasted coffee beans, both regular and decaffeinated. The company expects to have this new extension operational by the start of next year, when it will expand its product portfolio from the current one (only raw coffee beans) to also include the new products (raw coffee beans, roasted coffee beans and decaffeinated coffee beans).
 Roasted coffee beans are obtained by placing raw coffee beans into cylinders where hot air is blown. Beans are heated to ~250ºC for around 12 minutes. The cylinder rotates to ensure that beans are roasted evenly.  On the other hand, decaff coffee can be obtained through several methods. Beanie Limited employs the chemical solvent method, which consists on steaming and rinsing the raw coffee beans with Ethyl Acetate. After removing the chemical agent, the beans are roasted just like regular coffee. The new facility comes with one limitation: there is only one cylinder production line. This means that, on any given day, it can only produce roasted or decaff coffee, but not both. Changing from one product to the other is a hefty task known as a changeover, which typically leaves the line out of order for some time. 
 Jeroen faces two challenges:
 - On one hand, he must decide how will the production line be managed regarding how capacity gets split between the two processed products, roasted and decaff beans.
 - On the other hand, the new changes also imply that Diemen's raw coffee beans stock will not only play the role of providing other regional DCs with the raw beans they need, but will also be a raw material for the processing facility. Jeroen thinks that his replenishment policy should be reviewed to ensure that it satisfies the current situation.
 In order to tackle this, Jeroen would like to receive proposals from you regarding the management of the production line and the policy to order shipments from Latin America. He trusts that your simulation and optimization skills will assist in providing a good solution, since the complexities of the operation have proven to be a tough bone for his team.
 ## Detailed task definition
 - Below you will find four levels of questions. Levels 1 to 3 are compulsory.
  Level 4 is optional.
 - You need to write a report document where you answer the questions of the
  different levels. This report should be directed towards Jeroen, should give
  her clear recommendations and should justify these recommendations. It's
  important for you to reflect your methodology to back your proposals.
 - Each level is worth 2 points out of a total of 10. The 2 missing points will
  grade the clarity and structure of your report and code.
 - You need to use a Python notebook to solve all levels. A helper notebook is
  provided. Please attach a notebook that shows your
  solution/proposal/analysis.
 - Include your team number, names and student IDs in all your deliverables.
 ## Data and other facts
 A few general facts provided by Jeroen's team:
 - The production line can roast 125.000 daily kilograms of coffee, or 70.000 daily kilograms if it's decaffeinated.
 - Since the company was not selling roasted and decaf coffee before, there is no historical data for sales. Nevertheless, these are the forecasts you have received:
 	- Jeroen expects to receive about 3 orders per week for roasted coffee. He expects each order to be somewhere between 200.000 and 300.000, with 250.000 being the expected "typical" amount.
 	- As for the decaff, he expects 1 order per week, with a similar sizing of the roasted coffee ones.
 - Switching the line from one product to another takes somewhere between 24 to 48 hours. This also includes stopping the line to not produce anything, and starting the line again.
 - You can assume that producing one kilogram of roasted coffee or one kilogram of decaff coffee consumes one kilogram of raw coffee beans.
 - Jeroen's team have indicated that you should ensure that the line always runs production batches of at least 5 days. This means, once the line gets prepared for a specific product, it should produce for at least 5 days before switching to another product or stopping. Running batches of more than 5 days is perfectly fine.
 - Only one order from Latin America to Diemen can be active at the same time.
 You have also received two tables that contain real data from the past 2 years:
 - served_orders: this table shows every order that was served by the Diemen DC in the past years. This means, each record corresponds to one request of raw coffee beans that one of the regional DCs placed to Diemen and Diemen served. The units are kilograms.
 - sourcing_events: this table shows the Purchase Orders Diemen placed to it's Latin American providers. For each order, there are two dates: the date when
  the order was placed, and the date where the beans actually reached Diemen. The units are kilograms.
 ## Notebook
 A notebook with some helping code has been provided. The code contains a small
 simulation engine that can help you simulate a year of activity for the
 distribution center. The instructions on how to use the code are in the notebook itself.
 ## Levels
 - Level 1
 	- Jeroen wants you to provide a purchasing policy and a production line policy. This means your policies define when to buy more beans and how much, and what production should be on the production line everyday.
 	- He would like to achieve a service level of 99% for raw coffee beans, and of 95% for roasted and decaff beans.
 - Level 2
 	- As part of the new processing facility, additional warehousing space will also be added to the Diemen DC. Currently, the location can hold up to 20,000 tons of beans. Jeroen would like your advice on how much additional storage should be built to guarantee that the warehouse never exceeds its capacity.
 - Level 3
 	- Traditionally, Jeroen's team has followed a rule of only having one shipment coming from Latin America to Diemen at the same time, mostly for simplicity's sake. Nevertheless, Jeroen is wondering: how much would allowing up to 3 shipments to be on the way simultaneously help Diemen? Would it improve any metrics significantly?
 	- Bear in mind that any orders placed to Latin American providers should be of, at least, 3000 tons of beans.
 - Level 4
 	- Out of the three products that Diemen will handle, decaffeinated beans are the smallest one in volume and relevance. Jeroen is wondering: if the service level was downgraded from the original 95% to 75%, how much would that benefit the DC and its metrics?
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