Problem_RPQ.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM68yzb0/uBNqoeGeLb6g9W",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jakubdaron/MTR_optymalizacja/blob/main/Problem_RPQ.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "fpVymJ0T5ZTC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0d0aa6ce-42fe-49fc-9b18-f0ec62142d22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 23  37 107]\n",
            " [ 92  39 145]\n",
            " [ 99  29  15]\n",
            " [ 99  13 217]\n",
            " [106   8  83]\n",
            " [119  14 128]\n",
            " [140   6 107]\n",
            " [153   9 273]\n",
            " [162  27 145]\n",
            " [166   3  40]\n",
            " [177  24 122]\n",
            " [179  14 254]\n",
            " [256  42 106]\n",
            " [269   1  48]\n",
            " [273  30 206]\n",
            " [290  47   1]\n",
            " [295   5 107]\n",
            " [298  31  82]\n",
            " [310  22  24]\n",
            " [311   9 158]]\n",
            "Cmax: 465\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def generate_sorted_rpq_table(n, F):\n",
        "    rpq = []\n",
        "    # generate random r, p, q values\n",
        "    for i in range(n):\n",
        "        r = random.randint(0, n*F)\n",
        "        p = random.randint(1, 50)\n",
        "        q = random.randint(0, n*F)\n",
        "        rpq.append([r, p, q])\n",
        "\n",
        "    # sort rpq table in increasing order of r values\n",
        "    rpq = np.array(rpq)\n",
        "    sorted_rpq = rpq[rpq[:, 0].argsort()]\n",
        "    #sorted_rpq = np.array([[6, 4, 2], [6, 8, 3], [10, 2, 3], [12, 3, 0]])\n",
        "    return sorted_rpq\n",
        "\n",
        "def add_and_sort_bufor(sorted_rpq, iteration, bufor_queue):\n",
        "    bufor = sorted_rpq[iteration].tolist()\n",
        "    bufor_queue.append(bufor)\n",
        "    bufor_queue = np.array(bufor_queue)\n",
        "\n",
        "    # sort bufor_queue in decreasing order of q values\n",
        "    sorted_bufor = bufor_queue[bufor_queue[:, 2].argsort()[::-1]]\n",
        "    bufor_queue = sorted_bufor.tolist()\n",
        "    return bufor_queue\n",
        "\n",
        "def plot_gantt_chart(ax, schedule):\n",
        "    tasks = [f'Task {i+1}' for i in range(len(schedule))]\n",
        "\n",
        "    for i, task in enumerate(schedule):\n",
        "        schedule[i][1], schedule[i][0] = task\n",
        "        ax.barh(tasks[i], schedule[i][1] - schedule[i][0], left=schedule[i][0], color='blue')\n",
        "\n",
        "    ax.set_xlabel('Time')\n",
        "    ax.set_ylabel('Tasks')\n",
        "    ax.set_title('Gantt Chart')\n",
        "    ax.invert_yaxis()  # Invert the y-axis to have Task 1 at the top\n",
        "\n",
        "def print_r(ax, sorted_rpq):\n",
        "    y = [1] * len(sorted_rpq)\n",
        "    x = sorted_rpq[:, 0]\n",
        "\n",
        "    ax.bar(x, y, color='red')\n",
        "\n",
        "    ax.set_xlabel('Moments in time (r)')\n",
        "    ax.set_title('Diagram of moments in time')\n",
        "\n",
        "def optimise_function(sorted_rpq, n):\n",
        "\n",
        "    schedule = []\n",
        "\n",
        "    iteration = 0\n",
        "    bufor_queue = []\n",
        "    current_task = 0\n",
        "    time = 0\n",
        "    end_iteration = 0\n",
        "    end_def = 0\n",
        "\n",
        "    while end_def == 0:\n",
        "        #print(current_task)\n",
        "        if time == sorted_rpq[iteration, 0]:\n",
        "            while True:\n",
        "                bufor_queue = add_and_sort_bufor(sorted_rpq, iteration, bufor_queue)\n",
        "                if iteration < n-1:\n",
        "                    iteration += 1\n",
        "                else:\n",
        "                    end_iteration = 1\n",
        "\n",
        "                if sorted_rpq[iteration, 0] != sorted_rpq[iteration-1, 0]:\n",
        "                    break\n",
        "\n",
        "        if current_task <= 0 and bufor_queue:\n",
        "            current_task = bufor_queue[0][1]\n",
        "            schedule.append([time, time+bufor_queue[0][1]])\n",
        "            bufor_queue.pop(0)\n",
        "\n",
        "        #print(f\"time: {time}, current_task: {current_task}, bufor_queue: {bufor_queue}\")\n",
        "        #print(f\"time: {time}, current_task: {current_task}\")\n",
        "        time += 1\n",
        "        current_task -= 1\n",
        "\n",
        "        if end_iteration == 1:\n",
        "            if current_task == 0 and len(bufor_queue) == 0:\n",
        "                end_def = 1\n",
        "\n",
        "    Cmax = time\n",
        "    print(f'Cmax: {Cmax}')\n",
        "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10))\n",
        "    plot_gantt_chart(ax1, schedule)\n",
        "    print_r(ax2, sorted_rpq)\n",
        "    plt.show()\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    F = 17\n",
        "    n = 20\n",
        "    sorted_rpq = generate_sorted_rpq_table(n, F)\n",
        "    print(sorted_rpq)\n",
        "    optimise_function(sorted_rpq, n)\n",
        "\n",
        "\n"
      ]
    }
  ]
}