thingies

.gitignore

@@ -1 +1,2 @@
-venv/
+venv/
+.obsidian

README.md

@@ -0,0 +1,22 @@
+# Applied Optimization and Machine Learning
+
+This repo contains all the materials I use while giving the course Applied
+Optimization and Machine Learning techniques and its sibling Practical Data
+Science for Operations Management during year 2022/2023.
+
+It contains the following materials:
+
+- Lecture notes: the different notes I used in the classes.
+- Cases: the three cases that students had to do during the course. Each case
+  includes a narrative description, data and some helping code. The folders
+  also contain the grading details.
+- Exam: the exam, along with the grading helper to calculate the final scores
+  of students.
+- Other: a bit of everything.
+    - Random notes
+    - Memories I want to keep.
+    - Interesting materials that I did not directly use during the course.
+    - Ideas for next year.
+
+The repository also contains all files from previous editions in the `archive`
+folder.

aot/aot panel.md

@@ -0,0 +1,17 @@
+
+
+
+| 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                                                                                                                                                                                                                                                                                            |     |     |
+

aot/cases/case_1/case_1_student_notebook.ipynb

@@ -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": []
+    }
+  ]
+}

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.
+

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

archive/2122/cases/case_1/case_1_description.md

@@ -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.
+
+

archive/2122/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

archive/2122/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
+2021-08-04,2021-08-10,336022.8514088414
+2021-01-05,2021-01-10,351041.68228418275
+2021-04-29,2021-05-06,338705.4533610672
+2021-06-14,2021-06-22,216774.79936204778
+2021-06-21,2021-07-01,331075.5096480785
+2021-12-01,2021-12-07,358629.8535678753
+2021-12-13,2021-12-23,237124.0787089357
+2021-12-17,2021-12-22,229278.15299078176
+2021-10-17,2021-10-22,258928.96346443848
+2021-11-28,2021-12-05,378532.9798112182
+2021-06-10,2021-06-16,197717.59182534326
+2021-02-22,2021-02-27,384000.6974037471
+2021-11-30,2021-12-11,355168.6944729242
+2021-02-03,2021-02-10,312383.0537738918
+2021-04-13,2021-04-25,231816.99643375044
+2021-06-07,2021-06-13,276881.9915723157
+2021-12-03,2021-12-07,380847.56715868326
+2021-11-29,2021-12-09,412123.5063860729
+2021-03-29,2021-04-04,261409.7021051771
+2021-01-28,2021-02-05,404557.69495151856
+2021-07-04,2021-07-09,374522.25600175356
+2021-01-13,2021-01-20,328894.06129062787
+2021-09-29,2021-10-04,280742.72595198866
+2021-10-18,2021-10-25,291048.90802077635
+2021-04-09,2021-04-16,449418.2981764818
+2021-04-14,2021-04-22,341098.18303366995
+2021-05-08,2021-05-17,416941.3633993643
+2021-05-19,2021-05-27,345255.5746514472
+2021-07-26,2021-08-03,299525.71274023474
+2021-10-24,2021-10-31,367817.7882031555
+2021-01-14,2021-01-21,471478.60787775513

archive/2122/cases/case_1/stock_state.csv

@@ -0,0 +1,366 @@
+date,amount_in_stock
+2021-01-01,647479.2516513831
+2021-01-02,611270.6151648902
+2021-01-03,533486.4423972561
+2021-01-04,457004.6287930397
+2021-01-05,404698.75220385677
+2021-01-06,347600.1878352536
+2021-01-07,306034.5007738682
+2021-01-08,224039.00015402416
+2021-01-09,152997.08548998268
+2021-01-10,472251.5969659814
+2021-01-11,439516.50062731595
+2021-01-12,406661.0550947753
+2021-01-13,351240.1210121491
+2021-01-14,302356.80974864156
+2021-01-15,253988.21197549443
+2021-01-16,213762.2171895817
+2021-01-17,144758.54995178233
+2021-01-18,77379.61626667128
+2021-01-19,17935.183637817092
+2021-01-20,292387.4407724763
+2021-01-21,711069.23392869
+2021-01-22,680876.0831249541
+2021-01-23,618547.5455593257
+2021-01-24,574857.225400806
+2021-01-25,710652.8900352417
+2021-01-26,662858.7507577738
+2021-01-27,1090650.664424961
+2021-01-28,1026017.4875181256
+2021-01-29,958645.8244156246
+2021-01-30,907508.7560427187
+2021-01-31,845315.8242601342
+2021-02-01,782934.5790253138
+2021-02-02,720190.5475699984
+2021-02-03,668884.8415464258
+2021-02-04,616266.1743539482
+2021-02-05,968862.7606461754
+2021-02-06,913708.4863026484
+2021-02-07,873908.8571263258
+2021-02-08,808421.8782180586
+2021-02-09,753066.6459285829
+2021-02-10,1019238.2219733722
+2021-02-11,1313576.0418210896
+2021-02-12,1282039.0065675988
+2021-02-13,1235591.2856686988
+2021-02-14,1171860.2539031678
+2021-02-15,1117405.48380467
+2021-02-16,1076745.9766019788
+2021-02-17,1013251.97847048
+2021-02-18,958320.7118255846
+2021-02-19,930041.9769480445
+2021-02-20,893662.3380808627
+2021-02-21,831459.5798203176
+2021-02-22,776936.3696853134
+2021-02-23,734540.5173158804
+2021-02-24,691606.0919001552
+2021-02-25,640111.3214238406
+2021-02-26,595891.025376767
+2021-02-27,934221.6023643162
+2021-02-28,877114.1085506395
+2021-03-01,848141.8744925237
+2021-03-02,805093.1398847107
+2021-03-03,763587.6058287523
+2021-03-04,715661.57034632
+2021-03-05,654382.5748560184
+2021-03-06,615338.0743317754
+2021-03-07,578195.4376780196
+2021-03-08,518810.4274615445
+2021-03-09,779919.5351500085
+2021-03-10,737043.7148174229
+2021-03-11,699745.620588449
+2021-03-12,646333.721569387
+2021-03-13,600987.7229182889
+2021-03-14,547776.3167563358
+2021-03-15,506801.91593977675
+2021-03-16,450776.2401082926
+2021-03-17,407818.3558973169
+2021-03-18,342353.36305987765
+2021-03-19,313724.5858530795
+2021-03-20,262851.4550763895
+2021-03-21,215636.33972634672
+2021-03-22,170653.85826396095
+2021-03-23,97808.41041784057
+2021-03-24,61151.12686222342
+2021-03-25,25218.502460950258
+2021-03-26,-65584.03503789403
+2021-03-27,-119734.39702784433
+2021-03-28,146950.75604798403
+2021-03-29,97153.21441905304
+2021-03-30,57310.64009173471
+2021-03-31,10662.581871622475
+2021-04-01,-27588.618743333966
+2021-04-02,-82557.57021438742
+2021-04-03,-121780.90689560089
+2021-04-04,84432.07206712157
+2021-04-05,24238.10583310143
+2021-04-06,-83552.86652671939
+2021-04-07,-142480.2219082731
+2021-04-08,-195050.74612612266
+2021-04-09,41588.074514322594
+2021-04-10,-11364.844023714257
+2021-04-11,-66482.1236459639
+2021-04-12,-128813.02604588126
+2021-04-13,-189165.18592154793
+2021-04-14,-235646.37892092072
+2021-04-15,-262386.42745492875
+2021-04-16,128207.1126242844
+2021-04-17,484230.47263176995
+2021-04-18,415065.4991949436
+2021-04-19,350012.5057265632
+2021-04-20,263063.87403928395
+2021-04-21,215462.95198873535
+2021-04-22,502931.7009489148
+2021-04-23,444128.84454191074
+2021-04-24,416851.5529121987
+2021-04-25,575677.4656509105
+2021-04-26,798803.565254669
+2021-04-27,756329.9209084371
+2021-04-28,693343.5929958853
+2021-04-29,633572.7242262983
+2021-04-30,586605.6140128067
+2021-05-01,555322.3617422794
+2021-05-02,510051.40041188453
+2021-05-03,469851.3389004902
+2021-05-04,441081.9000312464
+2021-05-05,399484.615634201
+2021-05-06,696136.4720517738
+2021-05-07,664079.5714129838
+2021-05-08,639953.3389006793
+2021-05-09,578078.3984912352
+2021-05-10,508496.2163839203
+2021-05-11,453782.5063949912
+2021-05-12,375795.88872781984
+2021-05-13,324748.85745296953
+2021-05-14,265033.5293814591
+2021-05-15,191345.33714884822
+2021-05-16,125538.30636832467
+2021-05-17,486699.9090717564
+2021-05-18,433044.6008993777
+2021-05-19,379181.34503853624
+2021-05-20,336733.4798502792
+2021-05-21,302099.29447029356
+2021-05-22,220713.48533501168
+2021-05-23,167038.98676838083
+2021-05-24,134908.53922642054
+2021-05-25,99766.58410338087
+2021-05-26,64386.80915679069
+2021-05-27,361919.1602337716
+2021-05-28,311015.7070846562
+2021-05-29,258417.9931968885
+2021-05-30,220655.1474713701
+2021-05-31,188967.8022209354
+2021-06-01,187586.8123219715
+2021-06-02,148201.8543062532
+2021-06-03,85794.10557071284
+2021-06-04,19937.2721774291
+2021-06-05,-21222.25646840773
+2021-06-06,-99515.04498656568
+2021-06-07,-144600.11278759915
+2021-06-08,-208569.31457434213
+2021-06-09,-270434.79377998784
+2021-06-10,-146080.38556497518
+2021-06-11,-197744.22441062317
+2021-06-12,-247206.8338239739
+2021-06-13,-19232.408561805147
+2021-06-14,-47054.57870629374
+2021-06-15,-82331.94894057448
+2021-06-16,56822.28522437127
+2021-06-17,-5380.359315638307
+2021-06-18,-64742.15657142063
+2021-06-19,-102359.69861953295
+2021-06-20,-187079.5771196356
+2021-06-21,-237233.0730349294
+2021-06-22,-60781.477353804774
+2021-06-23,-95743.53688423763
+2021-06-24,219265.19183468865
+2021-06-25,166018.31299095904
+2021-06-26,127324.35545632138
+2021-06-27,62794.68059832805
+2021-06-28,21668.2514308655
+2021-06-29,-50316.4801029578
+2021-06-30,-79204.52348730947
+2021-07-01,191577.08330515135
+2021-07-02,141760.78489785508
+2021-07-03,98948.39846553442
+2021-07-04,64038.65918802995
+2021-07-05,26447.1233413148
+2021-07-06,-13022.080250524916
+2021-07-07,285296.5291195527
+2021-07-08,257249.25334153447
+2021-07-09,589937.2502111658
+2021-07-10,547332.2642310483
+2021-07-11,489625.47496736515
+2021-07-12,451659.6340056894
+2021-07-13,408940.087223126
+2021-07-14,372398.90279541345
+2021-07-15,327219.6904202084
+2021-07-16,278112.5707611354
+2021-07-17,249564.69143054486
+2021-07-18,199849.93454908518
+2021-07-19,633081.1550744908
+2021-07-20,560950.8118202476
+2021-07-21,885190.5961887846
+2021-07-22,838076.0106605014
+2021-07-23,775211.1163124711
+2021-07-24,726945.8405482947
+2021-07-25,644089.006161145
+2021-07-26,941593.1369481836
+2021-07-27,910279.2186288635
+2021-07-28,858576.4584500947
+2021-07-29,1188692.7272715634
+2021-07-30,1145293.3945720182
+2021-07-31,1089868.8541963038
+2021-08-01,1047642.9074704085
+2021-08-02,1012835.3742754249
+2021-08-03,1238891.4321784496
+2021-08-04,1211681.9816677598
+2021-08-05,1154444.895439112
+2021-08-06,1109275.8181821972
+2021-08-07,1058247.3735601068
+2021-08-08,1017256.953908889
+2021-08-09,985824.1863912917
+2021-08-10,1275142.1161176958
+2021-08-11,1207694.6598353714
+2021-08-12,1151633.8969831534
+2021-08-13,1115537.8540568247
+2021-08-14,1033378.6927169448
+2021-08-15,971994.1544095458
+2021-08-16,929073.1323963873
+2021-08-17,871498.3232116804
+2021-08-18,1108483.0617190541
+2021-08-19,1070370.8727955446
+2021-08-20,1027068.5970765499
+2021-08-21,1361247.2714829298
+2021-08-22,1309791.1082427092
+2021-08-23,1258414.6965946767
+2021-08-24,1212790.1028415759
+2021-08-25,1166055.3208899843
+2021-08-26,1128490.2460538154
+2021-08-27,1104935.8483842565
+2021-08-28,1084330.9002424532
+2021-08-29,1017996.6412879428
+2021-08-30,969948.7871034581
+2021-08-31,913757.0731994106
+2021-09-01,1250598.2141536232
+2021-09-02,1204568.0666521925
+2021-09-03,1193864.2432135388
+2021-09-04,1123520.6427849764
+2021-09-05,1053913.50147074
+2021-09-06,996981.9493567909
+2021-09-07,942318.3358728208
+2021-09-08,884592.806864691
+2021-09-09,830835.4141095028
+2021-09-10,777354.6650491382
+2021-09-11,727752.3731808765
+2021-09-12,674766.4777472744
+2021-09-13,637354.7405956141
+2021-09-14,908897.880879431
+2021-09-15,886906.8587683073
+2021-09-16,827398.073433537
+2021-09-17,785853.9417255967
+2021-09-18,738975.7754809558
+2021-09-19,706505.9460452488
+2021-09-20,644699.676989112
+2021-09-21,588624.9513246977
+2021-09-22,535141.1458672826
+2021-09-23,505063.9366007591
+2021-09-24,431993.38810376957
+2021-09-25,387875.01040075195
+2021-09-26,353740.67433509044
+2021-09-27,292121.1635336504
+2021-09-28,262786.2040530068
+2021-09-29,207945.42564793542
+2021-09-30,153084.1661070135
+2021-10-01,115417.47204050998
+2021-10-02,69902.58229749798
+2021-10-03,32029.986340895797
+2021-10-04,257910.21700555782
+2021-10-05,185741.80633443507
+2021-10-06,127330.03843891155
+2021-10-07,62879.39650024672
+2021-10-08,23033.226504836108
+2021-10-09,-27979.69656548275
+2021-10-10,-84173.66837961711
+2021-10-11,196022.73780922336
+2021-10-12,137816.2770916728
+2021-10-13,91328.3314459105
+2021-10-14,41251.62959627359
+2021-10-15,-6329.08472873627
+2021-10-16,-60244.91381143461
+2021-10-17,-99851.2698825248
+2021-10-18,-173641.52212470508
+2021-10-19,-212049.14390664152
+2021-10-20,-245454.1192965511
+2021-10-21,-305386.0794143668
+2021-10-22,-120943.28550890283
+2021-10-23,-171138.31388707145
+2021-10-24,-211211.51691554562
+2021-10-25,15210.595103964442
+2021-10-26,-46516.747972695215
+2021-10-27,-108704.63530860818
+2021-10-28,-162919.51332463368
+2021-10-29,-230061.855542359
+2021-10-30,-276549.55492150894
+2021-10-31,60181.492596672266
+2021-11-01,11902.539218675753
+2021-11-02,-8816.14378848672
+2021-11-03,-66954.54444227618
+2021-11-04,-116676.84740239033
+2021-11-05,-153418.98585937332
+2021-11-06,-196155.47476637954
+2021-11-07,-251512.16333905573
+2021-11-08,-333344.50629424525
+2021-11-09,-4059.411425020022
+2021-11-10,-43261.74829909939
+2021-11-11,-106048.24832104275
+2021-11-12,-143774.93807254065
+2021-11-13,-210808.42267524963
+2021-11-14,-266175.2330804739
+2021-11-15,-320073.4749942003
+2021-11-16,-382919.4569090524
+2021-11-17,-416753.2852401128
+2021-11-18,-477830.28394004394
+2021-11-19,-529058.3960308388
+2021-11-20,-584693.8663060239
+2021-11-21,-656496.8774633836
+2021-11-22,-398005.2872290472
+2021-11-23,-458715.2946404286
+2021-11-24,-531964.3107156917
+2021-11-25,-589663.322212392
+2021-11-26,-636461.6149367143
+2021-11-27,-672820.8031147933
+2021-11-28,-734332.3240520869
+2021-11-29,-773617.0527816914
+2021-11-30,-802079.1205139998
+2021-12-01,-525354.9727204265
+2021-12-02,-584780.1553593907
+2021-12-03,-627794.2090558368
+2021-12-04,-680563.7169338213
+2021-12-05,-358688.0285447965
+2021-12-06,-417863.17287740955
+2021-12-07,-94466.31801389478
+2021-12-08,-120354.06949547637
+2021-12-09,270468.6405604635
+2021-12-10,220782.1644699257
+2021-12-11,525267.2813442927
+2021-12-12,482176.8629086895
+2021-12-13,453361.41842871887
+2021-12-14,421915.6790918901
+2021-12-15,370951.4788054582
+2021-12-16,333261.71358073386
+2021-12-17,293991.26921963337
+2021-12-18,230223.3400138117
+2021-12-19,163977.57136618256
+2021-12-20,99667.54491378451
+2021-12-21,79481.0786327979
+2021-12-22,250930.1081393262
+2021-12-23,426844.782771413
+2021-12-24,366615.4882019885
+2021-12-25,308825.2904883709
+2021-12-26,248976.98635886348
+2021-12-27,200133.51200007505
+2021-12-28,154650.06743391437
+2021-12-29,109288.25307168259
+2021-12-30,52184.36661215486
+2021-12-31,2705.043157733511

archive/2122/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
+0.8,0.8,0.0684931506849315,0.0726027397260274,0.0222201370969798,10,-1594714.1742160032,-1530433.4046675265,285066.2951297739,10,50.2,50.0,1.3984117975602024,10
+0.8,1.0,0.5816438356164383,0.6520547945205479,0.25980427056171707,10,-20433.232547621035,66493.16878082216,231209.05072598366,10,49.4,49.5,0.9660917830792962,10
+0.8,1.2,0.9356164383561645,0.9534246575342465,0.040805672352052756,10,260408.40724531026,264591.4202889103,34451.38595202987,10,42.5,42.5,0.5270462766947288,10
+1.0,0.8,0.11863013698630136,0.10547945205479452,0.06135803865213693,10,-1360077.266559611,-1421026.0243578043,341686.4813035888,10,50.7,50.5,1.3374935098492577,10
+1.0,1.0,0.6550684931506849,0.7821917808219179,0.2787601486044031,10,63898.06455496148,158892.80360797068,215553.80705682412,10,49.3,49.5,1.1595018087284057,10
+1.0,1.2,0.9904109589041095,0.9945205479452055,0.011854570763239407,10,360626.1147252074,359297.0984386411,24074.260208145475,10,42.1,42.0,0.5676462121975469,10
+1.2,0.8,0.14684931506849314,0.11095890410958904,0.1127785478486988,10,-1471541.3243492458,-1590944.9289921233,402072.5562878826,10,50.1,50.5,1.7919573407620821,10
+1.2,1.0,0.8868493150684932,0.963013698630137,0.15021679044253444,10,289885.33637142787,340960.57915301056,116064.12202102688,10,49.9,50.0,0.7378647873726214,10
+1.2,1.2,0.996986301369863,1.0,0.007126799274932135,10,464072.69404238986,464252.8407907331,35729.24312964791,10,42.0,42.0,0.6666666666666669,10

archive/2122/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

archive/2122/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

archive/2122/cases/case_2/case_2_description.md

@@ -7,17 +7,17 @@ manufacturing and supply chain organization of Beanie Limited in Europe and
 reports directly to Beanie Limited's CEO.
 
 Given Charles role, he gets involved in larger, strategical and long-term
-decisions when we compare with Elisa. One of the hot topics that is currently
-in Charles mind is the next plan for coffee beans reception in European docks.
-Charles' office has reached out to you to prepare a report to assist in this
-highly important decision by using optimisation techniques.
+decisions when we compare with Elisa's domain. One of the hot topics that is
+currently in Charles mind is the next plan for coffee beans reception in
+European docks. Charles' office has reached out to you to prepare a report to
+assist in this highly important decision by using optimisation techniques.
 
-As you are probably aware, coffee beans are not grown in Europe. World
+As you are probably aware of, coffee beans are not grown in Europe. World
 production is divided across South/Central America, Africa and South East Asia.
 Beanie Limited purchases all of its coffee beans from different providers in
 America, mainly in Brasil and Colombia (these two countries account for almost
 40% of the world production). Beanie Limited procurement teams execute
-purchases with local providers at hose locations and then the coffee beans are
+purchases with local providers at those locations and then the coffee beans are
 shipped in container cargo ships to European docks.
 
 Docks are a natural bottleneck in supply chains. Dock capacity in europe is
@@ -47,7 +47,7 @@ Europe:
 - Valencia
 - Genoa
 
-In the upcoming months, Charles team must sign contracts with some of these
+In the upcoming months, Charles' team must sign contracts with some of these
 docks to ensure incoming capacity for the coffee beans coming from America.
 This means they will agree with the docks on the expected handled volumes and
 thus they must decide with which docks they want to work and how much volume
@@ -75,6 +75,9 @@ costs are as low as possible.
 - 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.
+- For the areas of the report where you have built an optimization model,
+  please provide a formal definition of the problem in mathematical terms. This
+  sould include the target function, decision variables and constraints.
 - Include your team number, names and student IDs in all your deliverables.
 
 ## Data
@@ -84,66 +87,106 @@ A few facts:
 - Charles' team estimates that Beanie Limited will need to receive 1,500,000
   metric tons of coffee beans in European docks during next year.
 - The category being negotiated is 40 feet containers. 40 feet containers have
-  a a capacity of 66 cubic meters.
+  a capacity of 66 cubic meters.
 - One cubic meter of coffee beans weights approximately 450 kgrs.
 
-Charles' team has shared with you a table with the prices proposed by the
-different docks for 40ft containers. You can also find the maximum capacity
-that the dock is willing to provide to Beanie Limited (the capacity is
-described as a count of 40ft containers).
+Charles' team has shared with you a table (prices_and_capacities.csv) with the
+next year's prices proposed by the different docks for 40ft containers. You can
+also find the maximum capacity that the dock is willing to provide to Beanie
+Limited (the capacity is described as a count of 40ft containers).
+
+## Notebook
+
+A notebook with some helping code has been provided. The code contains examples
+on how to use `pulp`, a python package for modeling and solving optimization
+problems. You can use it to get familiar with how to solve linear, integer and
+mixed programming problems.
 
 ## Levels
 
-Level 1
+### Level 1
 
-Explore historical data and current proposals. Do you think there is a chance
-to reduce costs? Why?
+Examine the data provided below and current proposals data. Do you think there
+is a chance to reduce costs? Why? And will next year's total cost lower?
 
-Level 2
+You can assume that last year, around 80% of Beanie Limited's containers
+entered through Rotterdam and the remaining 20% through Amsterdam. Total volume
+was around 24000 40ft containers. Although they don't have more granular
+numbers at hand, Charles mentioned that they had a total expense of 13,700,00€
+on docks fees.
+
+### Level 2
 
 With the price and capacity data provided for each dock, Charles would like you
 to find out what is the optimal distribution across the different options for
 next year. He is expecting you to provide the exact numbers, as well as the
 expected costs that would result from your proposal.
 
-Level 3
+### Level 3
 
 As it tends to be in these projects, sometimes new information appears in the
 middle of the project. Charles' team has reached out to let you know that they
-have some additional details from the different ports. The following docks have
-a sign-up fee. This means that, if Beanie Limited wants to do any business with
-them at all, they need to pay this fee. This fee is only paid once and is
-independent of the container volume that goes through the dock.
+have some additional details from the different ports.
+
+The following docks have a sign-up fee. This means that, if Beanie Limited
+wants to do any business with them at all, they need to pay this fee. This fee
+is only paid once and is independent of the container volume that goes through
+the dock.
 
 - Algeciras: 800,000€
 - Marseille: 500,000€
 - Antwerp: 1,000,000€
 
+Also, after presenting your initial results from level 2, Charles is very happy
+with your work, although there is something bothering him. Even though your
+proposal sounds reasonable, he is concerned that splitting the operations
+between too many docks might make land logistics operations too complex and end
+up causing higher costs in their truck transportation that cancel the potential
+savings that could be obtained in docks operations.
+
+Charles doesn't have numbers currently, so there is no way to tackle this
+properly in an empirical way. To have the necessary information to think about
+this in the future, he would like you to repeat your analysis with a new
+condition in place: to only use a maximum of 3 docks in your proposal. Charles
+wants to know what would be the optimal decision while restricting the used
+docks to that number.
+
 Charles wants you to reassess the situation with the new data and provide the
 adequate solution with the new options. He also would like to understand how
 these changes impact the solution you provided for level 2 and the overall
 results for Beanie Limited.
 
-Level 4
+### Level 4
 
-After presenting your results from level 3, Charles is very happy with your
-work, although there is something bothering him. Even though your proposal
-sounds reasonable, he is concerned that splitting the operations between too
-many docks might make European land logistics operations too complex and end up
-causing higher costs in their truck transportation that cancel the potential 
-savings that cana be obtained in docks operations.
+Now that it seemed you finally had a proper proposal in place, Charles is
+calling again with more concerns and asking you to go the extra mile with a
+final assessment.
 
-Charles doesn't have numbers currently, so there is no way to tackle this
-properly. To have the necessary information to think about this in the future,
-he would like you to repeat your analysis with a new condition in place: to
-only use a maximum of 3 docks in your proposal. Charles wants to know what
-would be the optimal decision while restricting the used docks to that number.
+Apparently, some rumours have reached Charles ears. It seems that some new
+regulations in Spain might affect taxes related to dock activities. Charles
+believes this could have an impact on the prices in Valencia and Algeciras,
+putting pressure on them to increase their prices because additional taxes
+would increase their own costs. The issue is: Charles doesn't know for sure
+what will happen. His expert opinion is that the following is possible:
 
-Finally, to spice things up, one of the analysts in Charles' team also calls
-you to inform that the management from the Rotterdam docks has warned Beanie
-Limited that they have a new minimum volume policy in place. They will only
-make business with Beanie Limited if they commit to handle, at least, 30000
-thousand containers through Rotterdam.
+- For Valencia, Charles thinks the price can end up somewhere between today's
+  price (310€/40ft) and 390€/40ft. He says the probability of any price is in
+  that range is equal.
+- For Algeciras, Charles thinks the price can end up somewhere between today's
+  price (280€/40ft) and 330€/40ft. Again, he says the probability of any price
+  is in that range is equal.
+
+Charles heard you did a great job by using simulation techniques with Elisa in
+your previous project. Charles' can't request you to do any simulation work as
+part of the current engagement, because that would be out of the scoped that
+was agreed upon. But nevertheless, he would appreciate if you could explain to
+him how would you use simulation to make the decision with these new
+uncertainty in place. What would be your methodology? What kind of results
+could you provide to him?
+
+With that information, he might decide to spend some more money with Simiupf to
+pursue that stream of work. And that would definetely make your bosses very
+happy!
 
 
 

archive/2122/cases/case_2/case_2_student_notebook.ipynb

@@ -0,0 +1,601 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "case_2_student_notebook.ipynb",
+      "provenance": [],
+      "toc_visible": true,
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Introduction to PuLP\n",
+        "\n",
+        "For case 2, 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": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ni6Q_YiO0nIm",
+        "outputId": "405d3f57-4502-4ed5-dcb9-d3204b585bb8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting pulp\n",
+            "  Downloading PuLP-2.6.0-py3-none-any.whl (14.2 MB)\n",
+            "\u001b[K     |████████████████████████████████| 14.2 MB 8.9 MB/s \n",
+            "\u001b[?25hInstalling collected packages: pulp\n",
+            "Successfully installed pulp-2.6.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "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": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ney2a8mu1JqQ",
+        "outputId": "a6b32d96-b163-4fc5-b4fc-3d5673e5a40a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss.........."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\t Test that logic put in place for deprecation handling of indexs works\n",
+            "\t Testing 'indexs' param continues to work for LpVariable.dicts\n",
+            "\t Testing 'indexs' param continues to work for LpVariable.matrix\n",
+            "\t Testing 'indices' argument works in LpVariable.dicts\n",
+            "\t Testing 'indices' param continues to work for LpVariable.matrix\n",
+            "\t Testing invalid status\n",
+            "\t Testing continuous LP solution - export dict\n",
+            "\t Testing export dict for LP\n",
+            "\t Testing export dict MIP\n",
+            "\t Testing maximize continuous LP solution\n",
+            "\t Testing continuous LP solution - export JSON\n",
+            "\t Testing continuous LP solution - export solver dict\n",
+            "\t Testing continuous LP solution - export solver JSON\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            ".........."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\t Testing reading MPS files - binary variable, no constraint names\n",
+            "\t Testing reading MPS files - integer variable\n",
+            "\t Testing reading MPS files - maximize\n",
+            "\t Testing invalid var names\n",
+            "\t Testing logPath argument\n",
+            "\t Testing makeDict general behavior\n",
+            "\t Testing makeDict default value behavior\n",
+            "\t Testing measuring optimization time\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "............."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\t Testing the availability of the function pulpTestAll\n",
+            "\t Testing zero subtraction\n",
+            "\t Testing inconsistent lp solution\n",
+            "\t Testing continuous LP solution\n",
+            "\t Testing maximize continuous LP solution\n",
+            "\t Testing unbounded continuous LP solution\n",
+            "\t Testing Long Names\n",
+            "\t Testing repeated Names\n",
+            "\t Testing zero constraint\n",
+            "\t Testing zero objective\n",
+            "\t Testing LpVariable (not LpAffineExpression) objective\n",
+            "\t Testing Long lines in LP\n",
+            "\t Testing LpAffineExpression divide\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "............."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\t Testing MIP solution\n",
+            "\t Testing MIP solution with floats in objective\n",
+            "\t Testing Initial value in MIP solution\n",
+            "\t Testing fixing value in MIP solution\n",
+            "\t Testing MIP relaxation\n",
+            "\t Testing feasibility problem (no objective)\n",
+            "\t Testing an infeasible problem\n",
+            "\t Testing an integer infeasible problem\n",
+            "\t Testing another integer infeasible problem\n",
+            "\t Testing column based modelling\n",
+            "\t Testing dual variables and slacks reporting\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "..........ssssssssssssssssssssssssssssssssssssssssssssssssssssss"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\t Testing fractional constraints\n",
+            "\t Testing elastic constraints (no change)\n",
+            "\t Testing elastic constraints (freebound)\n",
+            "\t Testing elastic constraints (penalty unchanged)\n",
+            "\t Testing elastic constraints (penalty unbounded)\n",
+            "\t Testing timeLimit argument\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss\n",
+            "----------------------------------------------------------------------\n",
+            "Ran 840 tests in 15.681s\n",
+            "\n",
+            "OK (skipped=784)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "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": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4Fbltpbp5mRi",
+        "outputId": "07f9c959-e9b0-4fe7-e7ea-c698703111ff"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "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": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8U4xVvUg9W07",
+        "outputId": "32a330f1-65ab-4903-f29b-368f2bacaf94"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Status: Optimal\n",
+            "x = 10.0\n",
+            "y = 50.0\n",
+            "z = 0.0\n",
+            "-150.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "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 lecture 6."
+      ],
+      "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": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d873d58d-6d9e-459e-d66f-127f436b1aab",
+        "id": "u1vI73kiXVLF"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Status: Optimal\n",
+            "Bread_grams = 300.0\n",
+            "Peanut_Butter_grams = 39.0\n",
+            "1090.32\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Case 2\n",
+        "\n",
+        "You can use the rest of the notebook to work on the different parts of case 2."
+      ],
+      "metadata": {
+        "id": "6kWgbTjU-LaN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Good luck!"
+      ],
+      "metadata": {
+        "id": "aYzseTWh-Sal"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}