RCPSP_Cummulative_CP.ipynb

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      "authorship_tag": "ABX9TyNE+Qe4lt7YpkHcijiBIJrs",
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/OptimizationExpert/Pyomo/blob/main/RCPSP_Cummulative_CP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_3hpwjuCYLL1"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1Kqt5KuYLw3",
        "outputId": "2e181d3a-021c-4ce3-ec80-ff091bb92a16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ortools\n",
            "  Downloading ortools-9.10.4067-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.9 kB)\n",
            "Collecting absl-py>=2.0.0 (from ortools)\n",
            "  Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\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",
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            "  Downloading protobuf-5.28.1-cp38-abi3-manylinux2014_x86_64.whl.metadata (592 bytes)\n",
            "Requirement already satisfied: immutabledict>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (4.2.0)\n",
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            "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",
            "Downloading ortools-9.10.4067-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.7/26.7 MB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading absl_py-2.1.0-py3-none-any.whl (133 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading protobuf-5.28.1-cp38-abi3-manylinux2014_x86_64.whl (316 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.6/316.6 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: protobuf, absl-py, ortools\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
            "      Successfully uninstalled protobuf-3.20.3\n",
            "  Attempting uninstall: absl-py\n",
            "    Found existing installation: absl-py 1.4.0\n",
            "    Uninstalling absl-py-1.4.0:\n",
            "      Successfully uninstalled absl-py-1.4.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 5.28.1 which is incompatible.\n",
            "google-ai-generativelanguage 0.6.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.1 which is incompatible.\n",
            "google-cloud-datastore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.28.1 which is incompatible.\n",
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            "tensorboard 2.17.0 requires protobuf!=4.24.0,<5.0.0,>=3.19.6, but you have protobuf 5.28.1 which is incompatible.\n",
            "tensorflow 2.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.28.1 which is incompatible.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.28.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed absl-py-2.1.0 ortools-9.10.4067 protobuf-5.28.1\n"
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = [\n",
        "    [0, 0, 0, 0, 0, 18, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 24, 25, 26, 27, 28, 29, 30, 31],\n",
        "    [10, 1, 0, 1, 1, 6, 23, 22, 19, 16, 15, 14],\n",
        "    [5, 4, 1, 6, 0, 3, 17, 14, 8],\n",
        "    [1, 6, 3, 2, 0, 3, 21, 15, 14],\n",
        "    [3, 3, 0, 4, 2, 3, 18, 15, 14],\n",
        "    [4, 0, 5, 2, 3, 3, 17, 15, 14],\n",
        "    [1, 5, 4, 0, 2, 2, 19, 10],\n",
        "    [2, 5, 5, 0, 4, 2, 20, 16],\n",
        "    [2, 0, 4, 1, 7, 2, 18, 17],\n",
        "    [4, 4, 0, 5, 6, 1, 16],\n",
        "    [6, 2, 5, 0, 2, 1, 17],\n",
        "    [4, 3, 5, 0, 2, 1, 15],\n",
        "    [5, 0, 4, 4, 2, 1, 14],\n",
        "    [6, 1, 3, 0, 2, 1, 32],\n",
        "    [7, 3, 3, 0, 2, 1, 32],\n",
        "    [10, 4, 7, 0, 3, 1, 32],\n",
        "    [1, 0, 4, 1, 2, 1, 32],\n",
        "    [1, 4, 0, 5, 6, 1, 32],\n",
        "    [8, 4, 4, 10, 0, 1, 32],\n",
        "    [8, 9, 0, 4, 4, 1, 32],\n",
        "    [1, 0, 5, 4, 7, 1, 32],\n",
        "    [6, 5, 4, 3, 0, 1, 32],\n",
        "    [9, 0, 5, 5, 4, 1, 32],\n",
        "    [9, 2, 4, 0, 3, 1, 32],\n",
        "    [10, 0, 1, 4, 8, 1, 32],\n",
        "    [5, 4, 0, 4, 7, 1, 32],\n",
        "    [5, 2, 0, 3, 3, 1, 32],\n",
        "    [8, 5, 0, 9, 4, 1, 32],\n",
        "    [7, 6, 0, 4, 5, 1, 32],\n",
        "    [7, 4, 0, 3, 5, 1, 32],\n",
        "    [9, 6, 4, 0, 8, 1, 32],\n",
        "    [0, 0, 0, 0, 0, 0]\n",
        "]\n",
        "\n",
        "\n",
        "cleaned = []\n",
        "for i in range(len(data)):\n",
        "  a = [data[i][0], data[i][1:5],data[i][6:] ]\n",
        "  print(i,a)\n",
        "  cleaned.append(a)\n",
        "\n",
        "\n",
        "df = pd.DataFrame(cleaned, columns=['duration','load', 'suc'])\n",
        "df"
      ],
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      "execution_count": null,
      "outputs": [
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            "0 [0, [0, 0, 0, 0], [2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 24, 25, 26, 27, 28, 29, 30, 31]]\n",
            "1 [10, [1, 0, 1, 1], [23, 22, 19, 16, 15, 14]]\n",
            "2 [5, [4, 1, 6, 0], [17, 14, 8]]\n",
            "3 [1, [6, 3, 2, 0], [21, 15, 14]]\n",
            "4 [3, [3, 0, 4, 2], [18, 15, 14]]\n",
            "5 [4, [0, 5, 2, 3], [17, 15, 14]]\n",
            "6 [1, [5, 4, 0, 2], [19, 10]]\n",
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            "8 [2, [0, 4, 1, 7], [18, 17]]\n",
            "9 [4, [4, 0, 5, 6], [16]]\n",
            "10 [6, [2, 5, 0, 2], [17]]\n",
            "11 [4, [3, 5, 0, 2], [15]]\n",
            "12 [5, [0, 4, 4, 2], [14]]\n",
            "13 [6, [1, 3, 0, 2], [32]]\n",
            "14 [7, [3, 3, 0, 2], [32]]\n",
            "15 [10, [4, 7, 0, 3], [32]]\n",
            "16 [1, [0, 4, 1, 2], [32]]\n",
            "17 [1, [4, 0, 5, 6], [32]]\n",
            "18 [8, [4, 4, 10, 0], [32]]\n",
            "19 [8, [9, 0, 4, 4], [32]]\n",
            "20 [1, [0, 5, 4, 7], [32]]\n",
            "21 [6, [5, 4, 3, 0], [32]]\n",
            "22 [9, [0, 5, 5, 4], [32]]\n",
            "23 [9, [2, 4, 0, 3], [32]]\n",
            "24 [10, [0, 1, 4, 8], [32]]\n",
            "25 [5, [4, 0, 4, 7], [32]]\n",
            "26 [5, [2, 0, 3, 3], [32]]\n",
            "27 [8, [5, 0, 9, 4], [32]]\n",
            "28 [7, [6, 0, 4, 5], [32]]\n",
            "29 [7, [4, 0, 3, 5], [32]]\n",
            "30 [9, [6, 4, 0, 8], [32]]\n",
            "31 [0, [0, 0, 0, 0], []]\n"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    duration           load                                                suc\n",
              "0          0   [0, 0, 0, 0]  [2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 24, 25, 26, ...\n",
              "1         10   [1, 0, 1, 1]                           [23, 22, 19, 16, 15, 14]\n",
              "2          5   [4, 1, 6, 0]                                        [17, 14, 8]\n",
              "3          1   [6, 3, 2, 0]                                       [21, 15, 14]\n",
              "4          3   [3, 0, 4, 2]                                       [18, 15, 14]\n",
              "5          4   [0, 5, 2, 3]                                       [17, 15, 14]\n",
              "6          1   [5, 4, 0, 2]                                           [19, 10]\n",
              "7          2   [5, 5, 0, 4]                                           [20, 16]\n",
              "8          2   [0, 4, 1, 7]                                           [18, 17]\n",
              "9          4   [4, 0, 5, 6]                                               [16]\n",
              "10         6   [2, 5, 0, 2]                                               [17]\n",
              "11         4   [3, 5, 0, 2]                                               [15]\n",
              "12         5   [0, 4, 4, 2]                                               [14]\n",
              "13         6   [1, 3, 0, 2]                                               [32]\n",
              "14         7   [3, 3, 0, 2]                                               [32]\n",
              "15        10   [4, 7, 0, 3]                                               [32]\n",
              "16         1   [0, 4, 1, 2]                                               [32]\n",
              "17         1   [4, 0, 5, 6]                                               [32]\n",
              "18         8  [4, 4, 10, 0]                                               [32]\n",
              "19         8   [9, 0, 4, 4]                                               [32]\n",
              "20         1   [0, 5, 4, 7]                                               [32]\n",
              "21         6   [5, 4, 3, 0]                                               [32]\n",
              "22         9   [0, 5, 5, 4]                                               [32]\n",
              "23         9   [2, 4, 0, 3]                                               [32]\n",
              "24        10   [0, 1, 4, 8]                                               [32]\n",
              "25         5   [4, 0, 4, 7]                                               [32]\n",
              "26         5   [2, 0, 3, 3]                                               [32]\n",
              "27         8   [5, 0, 9, 4]                                               [32]\n",
              "28         7   [6, 0, 4, 5]                                               [32]\n",
              "29         7   [4, 0, 3, 5]                                               [32]\n",
              "30         9   [6, 4, 0, 8]                                               [32]\n",
              "31         0   [0, 0, 0, 0]                                                 []"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>load</th>\n",
              "      <th>suc</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>[1, 0, 1, 1]</td>\n",
              "      <td>[23, 22, 19, 16, 15, 14]</td>\n",
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              "      <td>[17, 14, 8]</td>\n",
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              "      <th>3</th>\n",
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              "      <td>[21, 15, 14]</td>\n",
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              "      <th>4</th>\n",
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              "      <td>[3, 0, 4, 2]</td>\n",
              "      <td>[18, 15, 14]</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>[0, 5, 2, 3]</td>\n",
              "      <td>[17, 15, 14]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>[5, 4, 0, 2]</td>\n",
              "      <td>[19, 10]</td>\n",
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              "      <th>7</th>\n",
              "      <td>2</td>\n",
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              "      <td>[20, 16]</td>\n",
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              "      <th>8</th>\n",
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              "      <td>[18, 17]</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4</td>\n",
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              "      <td>[16]</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
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              "      <th>11</th>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
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              "      <td>[14]</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>6</td>\n",
              "      <td>[1, 3, 0, 2]</td>\n",
              "      <td>[32]</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>7</td>\n",
              "      <td>[3, 3, 0, 2]</td>\n",
              "      <td>[32]</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>10</td>\n",
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              "      <td>[32]</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
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              "      <td>[32]</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>[4, 0, 5, 6]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>8</td>\n",
              "      <td>[4, 4, 10, 0]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8</td>\n",
              "      <td>[9, 0, 4, 4]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>[0, 5, 4, 7]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>6</td>\n",
              "      <td>[5, 4, 3, 0]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>9</td>\n",
              "      <td>[0, 5, 5, 4]</td>\n",
              "      <td>[32]</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
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              "      <th>24</th>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5</td>\n",
              "      <td>[4, 0, 4, 7]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5</td>\n",
              "      <td>[2, 0, 3, 3]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8</td>\n",
              "      <td>[5, 0, 9, 4]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>7</td>\n",
              "      <td>[6, 0, 4, 5]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>7</td>\n",
              "      <td>[4, 0, 3, 5]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>9</td>\n",
              "      <td>[6, 4, 0, 8]</td>\n",
              "      <td>[32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0</td>\n",
              "      <td>[0, 0, 0, 0]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "        document.querySelector('#df-de8ba338-03b3-4eaf-bbc0-3b50e74178d0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-de8ba338-03b3-4eaf-bbc0-3b50e74178d0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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              "        height: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <script>\n",
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              "        document.querySelector('#id_04ec5311-dad2-45ad-a632-31dd0415b9b8 button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 32,\n  \"fields\": [\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          4,\n          0,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"load\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"suc\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "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\"]"
      ],
      "metadata": {
        "id": "jfSo0jdWIOJ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from __future__ import print_function\n",
        "from ortools.sat.python import cp_model as cp\n",
        "import math, sys\n",
        "# from cp_sat_utils import my_cumulative\n",
        "\n",
        "\n",
        "def main():\n",
        "\n",
        "  model = cp.CpModel()\n",
        "\n",
        "  #\n",
        "  # data\n",
        "  #\n",
        "  n = 4\n",
        "  N = len(df)\n",
        "  tasks = range(N)\n",
        "  duration = [d for d in df['duration']]\n",
        "  demand ={}\n",
        "  machines = range(n)\n",
        "\n",
        "  for m in machines:\n",
        "    demand[m] = [df.loc[task,'load'][m] for task in tasks]\n",
        "  upper_limit = 70\n",
        "\n",
        "  #\n",
        "  # declare variables\n",
        "  #\n",
        "  start_times = [\n",
        "      model.NewIntVar(0, upper_limit-duration[i], \"start_times[%i]\" % i) for i in tasks\n",
        "  ]\n",
        "  #end_times = [\n",
        "  #    model.NewIntVar(0, upper_limit * 2, \"end_times[%i]\" % i) for i in tasks\n",
        "  #]\n",
        "  end_time = model.NewIntVar(0, upper_limit, \"end_time\")\n",
        "\n",
        "  #intervals = [model.NewIntervalVar(start_times[i],duration[i],start_times[i]+ duration[i],f\"interval[{i}]\")  for i in tasks]\n",
        "  intervals = [model.NewFixedSizeIntervalVar(start_times[i],duration[i],f\"interval[{i}]\")  for i in tasks]\n",
        "\n",
        "\n",
        "  # number of needed resources, to be minimized\n",
        "\n",
        "  #\n",
        "  # constraints\n",
        "  #\n",
        "  #model.AddMaxEquality(end_time,end_times)\n",
        "  for machine in machines:\n",
        "    model.AddCumulative(intervals, demand[machine], 10)\n",
        "\n",
        "  for task in tasks:\n",
        "    model.Add(start_times[task]+duration[task]<= end_time)\n",
        "    for k in df.loc[task,'suc']:\n",
        "      model.Add(start_times[task]+duration[task]<=start_times[k-1] )\n",
        "\n",
        "\n",
        "\n",
        "  #\n",
        "  # Some extra constraints to play with\n",
        "  #\n",
        "\n",
        "  # all tasks must end within an hour\n",
        "\n",
        "  # All tasks should start at time 0\n",
        "  # for i in range(n):\n",
        "  #    model.Add(start_times[i] == 0)\n",
        "\n",
        "  # limitation of the number of people\n",
        "  # model.Add(num_resources <= 3)\n",
        "\n",
        "  #\n",
        "  # objective\n",
        "  #\n",
        "  model.Minimize(end_time)\n",
        "  #model.Minimize(end_time)\n",
        "\n",
        "  #\n",
        "  # solution and search\n",
        "  #\n",
        "  solver = cp.CpSolver()\n",
        "  solver.parameters.num_workers = 16\n",
        "\n",
        "  status = solver.Solve(model)\n",
        "  #\n",
        "  # result\n",
        "  #\n",
        "  if status == cp.OPTIMAL:\n",
        "    print(\"OPTIMAL\")\n",
        "    print(\"num_resources:\", solver.Value(end_time))\n",
        "    #print(\"start_times  :\", [solver.Value(start_times[i]) for i in range(n)])\n",
        "    #print(\"duration     :\", [duration[i] for i in range(n)])\n",
        "    #print(\"end_times    :\", [solver.Value(end_times[i]) for i in range(n)])\n",
        "    #print(\"end_time     :\", solver.Value(end_time))\n",
        "  elif status == cp.INFEASIBLE:\n",
        "    print(\"INFEASIBLE\")\n",
        "  elif status == cp.FEASIBLE:\n",
        "    print(\"FEASIBLE\")\n",
        "  elif status == cp.UNKNOWN:\n",
        "    print(\"UNKNOWN\")\n",
        "\n",
        "  # print(\"num_solutions:\", num_solutions)\n",
        "  print(\"NumConflicts:\", solver.NumConflicts())\n",
        "  print(\"NumBranches:\", solver.NumBranches())\n",
        "  print(\"WallTime:\", solver.WallTime())\n",
        "\n",
        "  plt.figure(figsize=(10,5))\n",
        "  for task in tasks:\n",
        "    st,fn = solver.value(start_times[task]), solver.value(start_times[task]) + duration[task]\n",
        "    plt.gca().add_patch(Rectangle((st,task), duration[task],1,linewidth=1,edgecolor='k',facecolor=KOLORS[task%10], alpha =0.5))\n",
        "    plt.text(0.5*(st+fn), task, s= str(task))\n",
        "\n",
        "  plt.xlim(0,solver.value(end_time)+2)\n",
        "  plt.ylim(0,32)\n",
        "  plt.yticks(tasks)\n",
        "  plt.xticks(range(70), rotation =90)\n",
        "\n",
        "  plt.grid()\n",
        "  plt.tight_layout()\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "  main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "tDNe-C7vYL5-",
        "outputId": "33d52e82-f3f5-4402-938e-564241127dab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FEASIBLE\n",
            "NumConflicts: 0\n",
            "NumBranches: 0\n",
            "WallTime: 202.39771601400003\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}