thingies

aot/cases/case_1/case_1_student_notebook.ipynb

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+{
+  "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_1/prices_and_capacities.csv

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+dock,40_ft_container_price_eur,max_capacity
+Rotterdam,470,33000
+Antwerp,470,25000
+Hamburg,480,44000
+Amsterdam,610,11000
+Marseille,380,9000
+Algeciras,280,20000
+Valencia,310,11000
+Genoa,340,7500