storage_routing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OptimizationExpert/Pyomo/blob/main/storage_routing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvJL0bOSIm84"
      },
      "source": [
        "Developed by Alireza Soroudi\n",
        "alireza.soroudi@gmail.com\n",
        "\n",
        "Linkedin News letter : https://www.linkedin.com/newsletters/optimization-in-open-source-6874020019009859585/\n",
        "\n",
        "Git hub: https://github.com/OptimizationExpert/Pyomo\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_3yuwV8XtMF",
        "outputId": "ffa09af4-20ee-45ec-85bf-2d450c3db73d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ortools in /usr/local/lib/python3.10/dist-packages (9.9.3963)\n",
            "Requirement already satisfied: absl-py>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (2.1.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from ortools) (1.25.2)\n",
            "Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (2.0.3)\n",
            "Requirement already satisfied: protobuf>=4.25.3 in /usr/local/lib/python3.10/dist-packages (from ortools) (5.26.1)\n",
            "Requirement already satisfied: immutabledict>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (4.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0.0->ortools) (1.16.0)\n",
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "!pip install ortools\n",
        "from ortools.sat.python import cp_model # CP-SAT solver\n",
        "import numpy as np\n",
        "from random import randint\n",
        "import random\n",
        "from math import ceil\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt # Data visualization\n",
        "from matplotlib.patches import Rectangle\n",
        "from google.colab import files\n",
        "\n",
        "import matplotlib.patches as patches\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AxZSoOv2-NEu"
      },
      "outputs": [],
      "source": [
        "KOLORS = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\",\n",
        "            \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\",\n",
        "            \"#aec7e8\", \"#ffbb78\", \"#98df8a\", \"#ff9896\", \"#c5b0d5\",\n",
        "            \"#c49c94\", \"#f7b6d2\", \"#c7c7c7\", \"#dbdb8d\", \"#9edae5\",\n",
        "            \"#8c6d31\", \"#9c9ede\", \"#637939\", \"#e7cb94\", \"#b5cf6b\",\n",
        "            \"#cedb9c\", \"#c7c7c7\", \"#bd9e39\", \"#e7969c\", \"#7b4173\"]\n",
        "\n",
        "def rects():\n",
        "  rectangle1 = patches.Rectangle((1.1, 0), 0.8, 9.1, edgecolor='k', facecolor='grey', alpha=0.5)\n",
        "  rectangle2 = patches.Rectangle((4.1, 0), 0.8, 9.1, edgecolor='k', facecolor='grey', alpha=0.5)\n",
        "  rectangle3 = patches.Rectangle((7.1, 0), 0.8, 9.1, edgecolor='k', facecolor='grey', alpha=0.5)\n",
        "  rectangle4 = patches.Rectangle((10.1, 0), 0.8, 9.1, edgecolor='k', facecolor='grey', alpha=0.5)\n",
        "\n",
        "  # Add the rectangle patch to the current axes\n",
        "  plt.gca().add_patch(rectangle1)\n",
        "  plt.gca().add_patch(rectangle2)\n",
        "  plt.gca().add_patch(rectangle3)\n",
        "  plt.gca().add_patch(rectangle4)\n",
        "  seen = []\n",
        "  for n in nodes:\n",
        "    x1,y1 = pos[n][0],pos[n][1]\n",
        "    if x1+1 in X and n not in seen:\n",
        "      plt.text(x1-0.5,y1,s=str(n))\n",
        "      seen.append(n)\n",
        "    elif x1+1 not in X and n not in seen:\n",
        "      plt.text(x1+0.2,y1,s=str(n))\n",
        "      seen.append(n)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X= [1, 2 , 4, 5, 7,8, 10,11]\n",
        "org =1\n",
        "Y = range(10)\n",
        "plt.figure(figsize=(8,5))\n",
        "counter = 0\n",
        "pos = {}\n",
        "for x in X:\n",
        "  for y in Y:\n",
        "    plt.scatter(x,y,s=50)\n",
        "    counter+=1\n",
        "    pos[counter]=(x,y)\n",
        "plt.xticks(X)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "nodes = [n for n in pos.keys()]\n",
        "m = 10\n",
        "filled= random.sample(nodes, m)\n",
        "filled = [76, 4, 31, 11, 67, 24, 43, 61, 29, 45]\n",
        "\n",
        "for n in filled:\n",
        "    x1,y1 = pos[n][0],pos[n][1]\n",
        "    plt.scatter(x1,y1,s=100, c='k', marker = 's')\n",
        "\n",
        "allowed = []\n",
        "for n in nodes:\n",
        "  for m in nodes:\n",
        "    con1 = n>m and pos[n][0]==pos[m][0] and abs(pos[n][1]-pos[m][1])==1\n",
        "    con2 = n>m and abs (X.index(pos[n][0])-X.index(pos[m][0]))==1 and pos[m][1]== min(Y) and pos[n][1]== min(Y)\n",
        "    con3 = n>m and abs (X.index(pos[n][0])-X.index(pos[m][0]))==1 and pos[m][1]== max(Y) and pos[n][1]== max(Y)\n",
        "    con4 = n>m and abs(pos[n][0]-pos[m][0])==2 and pos[m][1]== pos[n][1]\n",
        "    if con1 or con2 or con3 or con4:\n",
        "      x1,y1 = pos[n][0],pos[n][1]\n",
        "      x2,y2 = pos[m][0],pos[m][1]\n",
        "      allowed.append((n,m))\n",
        "      allowed.append((m,n))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Create a rectangle patch\n",
        "\n",
        "\n",
        "\n",
        "# Set the aspect of the plot to 'equal' to ensure the correct aspect ratio\n",
        "#plt.gca().set_aspect('equal', adjustable='box')\n",
        "rects()\n",
        "\n",
        "allowed =list(set(allowed))\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "print(len(allowed))\n",
        "\n",
        "def dist(i,j,pos):\n",
        "  return int(np.sqrt( (pos[i][0]-pos[j][0] )**2 + (pos[i][1]-pos[j][1] )**2 ) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "lqw1iTmuft6T",
        "outputId": "f66c1ea3-d1bc-44a5-ade6-95709fa54f77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "220\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = cp_model.CpModel()\n",
        "solver = cp_model.CpSolver()\n",
        "\n",
        "U = {(i,j):model.NewBoolVar(f\"connection_{i}_{j}\") for (i,j) in allowed}\n",
        "select = {i:model.NewBoolVar(f\"select_{i}\") for i in nodes  }\n",
        "U.update({(i,i):select[i].Not() for i in nodes  })\n",
        "\n",
        "for i in filled:\n",
        "  model.Add(select[i]==1)\n",
        "\n",
        "model.Add(select[1]==1)\n",
        "\n",
        "arcs = [(i,j,v) for (i,j),v in U.items() ]\n",
        "model.AddCircuit(arcs)\n",
        "\n",
        "\n",
        "# Minimize x\n",
        "expressions = [v*dist(i,j,pos) for (i,j),v in U.items() ]\n",
        "model.Minimize(sum(expressions))\n",
        "\n",
        "status = solver.Solve(model)\n",
        "if status == cp_model.FEASIBLE:\n",
        "  print(\"feasible\")\n",
        "elif status == cp_model.OPTIMAL:\n",
        "  print(\"optimal\")\n",
        "elif status == cp_model.INFEASIBLE:\n",
        "  print(\"INFEASIBLE\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1u74WJiO3-3W",
        "outputId": "395a310b-e491-4675-ceb4-e83c40643070"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "optimal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = cp_model.CpModel()\n",
        "solver = cp_model.CpSolver()\n",
        "\n",
        "U = {(i,j):model.NewBoolVar(f\"connection_{i}_{j}\") for (i,j) in allowed}\n",
        "select = {i:model.NewBoolVar(f\"select_{i}\") for i in nodes  }\n",
        "U.update({(i,i):select[i].Not() for i in nodes  })\n",
        "order_visit={i:model.NewIntVar(1,len(nodes), f\"order_{i}\") for i in nodes}\n",
        "\n",
        "\n",
        "\n",
        "for i in filled:\n",
        "  model.Add(select[i]==1)\n",
        "\n",
        "model.Add(select[1]==1)\n",
        "model.Add(order_visit[1]==1)\n",
        "\n",
        "arcs = [(i,j,v) for (i,j),v in U.items() ]\n",
        "model.AddCircuit(arcs)\n",
        "\n",
        "for (i,j),v in U.items():\n",
        "  if i!=j and j != org:\n",
        "    model.Add(order_visit[i]<order_visit[j]).OnlyEnforceIf(v)\n",
        "    model.Add(select[i]==1).OnlyEnforceIf(v)\n",
        "    model.Add(select[j]==1).OnlyEnforceIf(v)\n",
        "\n",
        "expressions_numbers = [order_visit[i] for i in nodes ]\n",
        "model.AddAllDifferent(expressions_numbers)\n",
        "\n",
        "o_list = [(45,31), (4,29)]\n",
        "for (f,s) in o_list:\n",
        "  model.Add(order_visit[f]<order_visit[s])\n",
        "\n",
        "for n in nodes:\n",
        "  model.Add(order_visit[n]<= sum(select[i] for i in nodes))\n",
        "\n",
        "# Maximize x\n",
        "expressions = [v*dist(i,j,pos) for (i,j),v in U.items() ]\n",
        "model.Minimize(sum(expressions))\n",
        "\n",
        "status = solver.Solve(model)\n",
        "if status == cp_model.FEASIBLE:\n",
        "  print(\"feasible\")\n",
        "elif status == cp_model.OPTIMAL:\n",
        "  print(\"optimal\")\n",
        "elif status == cp_model.INFEASIBLE:\n",
        "  print(\"INFEASIBLE\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INbSTRaaz1ta",
        "outputId": "3b3a80f0-381a-47e8-9ca0-b82d534d084a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "optimal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "counter = 0\n",
        "\n",
        "for n in filled:\n",
        "    x1,y1 = pos[n][0],pos[n][1]\n",
        "    plt.scatter(x1,y1,s=100, c='k', marker = 's', zorder=2)\n",
        "for x in X:\n",
        "  for y in Y:\n",
        "    plt.scatter(x,y,s=50)\n",
        "    counter+=1\n",
        "    pos[counter]=(x,y)\n",
        "\n",
        "org = 1\n",
        "start = 1\n",
        "finish = 1000\n",
        "rects()\n",
        "plt.xticks(X)\n",
        "plt.axis('off')\n",
        "counter = 0\n",
        "while finish !=org:\n",
        "  for (n,m),v in U.items():\n",
        "    if solver.Value(v)>0 and n==start:\n",
        "        x1,y1 = pos[n][0],pos[n][1]\n",
        "        x2,y2 = pos[m][0],pos[m][1]\n",
        "        plt.plot([x1,x2],[y1,y2], '--',lw=2,c='k', zorder=-1)\n",
        "        start = m\n",
        "        finish = m\n",
        "        counter+=1\n",
        "        name = f\"rectangle_{counter}.png\"\n",
        "        plt.savefig(f'/content/drive/My Drive/Colab Notebooks/img/rectangle_plot_{counter}.png')\n",
        "        if finish == org:\n",
        "          break\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "KVj_xEHrn2k0",
        "outputId": "2e18229d-7169-4b25-8d21-79c4bfe19eb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QP0N_Z-poozO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tmmtxX8_ZHW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJdhHr-DIlur"
      },
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}