JobShop_CP_ORTools_Idle_time.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/JobShop_CP_ORTools_Idle_time.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": 150,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_3yuwV8XtMF",
        "outputId": "a063660e-1f82-4f8d-ae5d-14aa3938297e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ortools in /usr/local/lib/python3.10/dist-packages (9.10.4067)\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) (1.26.4)\n",
            "Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (2.1.4)\n",
            "Requirement already satisfied: protobuf>=5.26.1 in /usr/local/lib/python3.10/dist-packages (from ortools) (5.27.3)\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) (2024.1)\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"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = [\n",
        "    [1, 3, 6, 7, 3, 6],\n",
        "    [8, 5, 10, 10, 10, 4],\n",
        "    [5, 4, 8, 9, 1, 7],\n",
        "    [5, 5, 5, 3, 8, 9],\n",
        "    [9, 3, 5, 4, 3, 1],\n",
        "    [3, 3, 9, 10, 4, 1]\n",
        "]\n",
        "\n",
        "# Define the row and column labels\n",
        "rows = [f'R{i+1}' for i in range(len(data))]\n",
        "columns = [f'J{j+1}' for j in range(len(data[0]))]\n",
        "\n",
        "# Create the DataFrame\n",
        "dfpr = pd.DataFrame(data, index=rows, columns=columns)\n",
        "dfpr\n",
        "\n",
        "data = [\n",
        "    [3, 1, 2, 4, 6, 5],\n",
        "    [2, 3, 5, 6, 1, 4],\n",
        "    [3, 4, 6, 1, 2, 5],\n",
        "    [2, 1, 3, 4, 5, 6],\n",
        "    [3, 2, 5, 6, 1, 4],\n",
        "    [2, 4, 6, 1, 5, 3]\n",
        "]\n",
        "order = {f\"R{r}\":[f'J{j}' for j in data[r-1]] for r in range(1,7)}\n",
        "order"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwwmYqvq8jK6",
        "outputId": "88dc1295-8dcb-45e2-e231-933253297737"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'R1': ['J3', 'J1', 'J2', 'J4', 'J6', 'J5'],\n",
              " 'R2': ['J2', 'J3', 'J5', 'J6', 'J1', 'J4'],\n",
              " 'R3': ['J3', 'J4', 'J6', 'J1', 'J2', 'J5'],\n",
              " 'R4': ['J2', 'J1', 'J3', 'J4', 'J5', 'J6'],\n",
              " 'R5': ['J3', 'J2', 'J5', 'J6', 'J1', 'J4'],\n",
              " 'R6': ['J2', 'J4', 'J6', 'J1', 'J5', 'J3']}"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "jobs = [j for j in dfpr.columns]\n",
        "machines = [r for r in dfpr.index]\n",
        "jobs, machines"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTaDitfE9Nnn",
        "outputId": "72210818-8c53-434b-8451-74279bbc77fa"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['J1', 'J2', 'J3', 'J4', 'J5', 'J6'], ['R1', 'R2', 'R3', 'R4', 'R5', 'R6'])"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "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\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "id": "4xH4VfXnamQQ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RApX4xchjMHD"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {
        "id": "yU46bQJO2WvI"
      },
      "outputs": [],
      "source": [
        "def plot_results():\n",
        "  m = (1+np.sqrt(5))*0.5\n",
        "  plt.figure(figsize=(4*m,4))\n",
        "  results_day = {}\n",
        "  all = []\n",
        "  allocated =[]\n",
        "  for m in machines:\n",
        "    y = machines.index(m) +1\n",
        "    #\n",
        "    for j in jobs:\n",
        "      x1,x2 = solver.Value(st[j,m]),solver.Value(st[j,m]) +  int(dfpr.loc[m,j])\n",
        "      jn= jobs.index(j)\n",
        "      plt.gca().add_patch(Rectangle((x1,y),  int(dfpr.loc[m,j]),0.4,linewidth=1,edgecolor='k',facecolor=KOLORS[jn], alpha =0.6))\n",
        "\n",
        "  #plt.yticks(range(len(actors)), actors, fontweight='bold')\n",
        "  #plt.xticks(all,allocated)\n",
        "  plt.xlim(0,0*solver.value(span)+110)\n",
        "  plt.ylim(0.2,7)\n",
        "  plt.yticks(range(1,1+len(machines)), machines)\n",
        "  plt.grid()\n",
        "  plt.tight_layout()\n",
        "  plt.xlabel(\"Time\")\n",
        "  plt.ylabel(\"Machine\")\n",
        "  plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "id": "yyF-IzKMcyrj"
      },
      "outputs": [],
      "source": [
        "def opt_schd(w1,w2):\n",
        "\n",
        "  model = cp_model.CpModel()\n",
        "  solver = cp_model.CpSolver()\n",
        "\n",
        "  st = {(j,m):model.NewIntVar(0,100, f\"scen_day_{j}_{m}\")  for j in jobs for m in machines}\n",
        "  uinterval = {(j,m):model.NewFixedSizeIntervalVar(v_st,int(dfpr.loc[m,j]), f\"interval_{j}_{m}\") for (j,m),v_st in st.items()}\n",
        "  span = model.NewIntVar(0,100, 'span')\n",
        "  min_m = {m:model.NewIntVar(0,100, f\"minstart_{m}\") for m in machines}\n",
        "  max_m = {m:model.NewIntVar(0,100, f\"maxend_{m}\") for m in machines}\n",
        "  job_order = {(j1,j2):model.NewBoolVar(f\"scen_day_{j1}_{j2}\")  for j1 in jobs for j2 in jobs if j1!=j2}\n",
        "\n",
        "  for (j1,j2),v in job_order.items():\n",
        "    if j1>j2:\n",
        "      model.AddExactlyOne([v,job_order[j2,j1]])\n",
        "  for m in machines:\n",
        "    expressions_st = [st[j,m] for j in jobs]\n",
        "    expressions_fn = [st[j,m]+int(dfpr.loc[m,j]) for j in jobs]\n",
        "    model.AddMinEquality(min_m[m],expressions_st)\n",
        "    model.AddMaxEquality(max_m[m],expressions_fn)\n",
        "    expressions = [uinterval[j,m] for j in jobs ]\n",
        "    model.AddNoOverlap(expressions)\n",
        "    for (j1,j2),v in job_order.items():\n",
        "      model.Add(st[j1,m] + dfpr.loc[m,j1]<= st[j2,m]).OnlyEnforceIf(job_order[j1,j2])\n",
        "\n",
        "  for m_index in range(1,len(machines)):\n",
        "    m1 = machines[m_index-1]\n",
        "    m2 = machines[m_index]\n",
        "    for j in jobs:\n",
        "      model.Add(st[j,m1] + dfpr.loc[m1,j] <= st[j,m2])\n",
        "\n",
        "  for m in machines:\n",
        "    expressions = [uinterval[j,m] for j in jobs ]\n",
        "    model.AddNoOverlap(expressions)\n",
        "    for (j1,j2),v in job_order.items():\n",
        "      model.Add(st[j1,m] + dfpr.loc[m,j1]<= st[j2,m]).OnlyEnforceIf(job_order[j1,j2])\n",
        "\n",
        "  for (j,m),v in st.items():\n",
        "    model.Add(v+dfpr.loc[m,j] <=span)\n",
        "\n",
        "\n",
        "  #model.Minimize(span)\n",
        "\n",
        "  expr_obj = [max_m[m]-min_m[m] for m in machines]\n",
        "  model.Minimize(w1*span + w2*sum(expr_obj))\n",
        "\n",
        "\n",
        "  solver.parameters.max_time_in_seconds = 60\n",
        "  status = solver.Solve(model)\n",
        "\n",
        "  if status == cp_model.OPTIMAL:\n",
        "    print(\"OPTIMAL\")\n",
        "  elif status == cp_model.FEASIBLE:\n",
        "    print(\"FEASIBLE\")\n",
        "  elif status == cp_model.INFEASIBLE:\n",
        "    print(\"INFEASIBLE\")\n",
        "    print(\"FEASIBLE\")\n",
        "  elif status == cp_model.UNKNOWN:\n",
        "    print(\"UNKNOWN\")\n",
        "  print(solver.ObjectiveValue())\n",
        "  print(solver.value(span), solver.value(sum(expr_obj)) )\n",
        "  return solver, st\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "machines[:-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BslIMsz3LTmp",
        "outputId": "3460db4e-3a8d-4246-c438-02ac58e48697"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['R1', 'R2', 'R3', 'R4', 'R5']"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "solver, st = opt_schd(1,0)\n",
        "plot_results()\n",
        "#for m in machines:\n",
        "#  assigned = [dfpr.loc[m,j] for j in jobs]\n",
        "#  print(m,sum(assigned) , solver.Value(max_m[m]), solver.Value(min_m[m]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "NfTrbCHyHyvM",
        "outputId": "d3b67d48-5aed-4b62-f258-b570b39c57fb"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OPTIMAL\n",
            "66.0\n",
            "66 253\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 647.214x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "solver, st = opt_schd(1,100)\n",
        "plot_results()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "aqjjBIhOQfb7",
        "outputId": "293858ab-0ad3-4008-b0f5-79b2060732e0"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FEASIBLE\n",
            "19775.0\n",
            "75 197\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 647.214x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CAptWaLqnk9z"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggDHmEaNbuH6"
      },
      "source": [
        "## Plot function"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}