{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "w4B3OoY-shn-", "pycharm": {} }, "source": [ "```{index} Gantt charts\n", "```\n", "```{index} disjunctive programming\n", "```\n", "```{index} single: application; job shop\n", "```\n", "```{index} single: application; scheduling\n", "```\n", "```{index} single: solver; highs\n", "```\n", "# Extra material: Job shop scheduling" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# install dependencies and select solver\n", "%pip install -q amplpy matplotlib\n", "\n", "SOLVER = \"highs\"\n", "\n", "from amplpy import AMPL, ampl_notebook\n", "\n", "ampl = ampl_notebook(\n", " modules=[\"highs\"], # modules to install\n", " license_uuid=\"default\", # license to use\n", ") # instantiate AMPL object and register magics" ] }, { "cell_type": "markdown", "metadata": { "id": "lffHCSpHshoF", "pycharm": {} }, "source": [ "## Job Shop Scheduling\n", "\n", "Job shop scheduling is one of the classic problems in Operations Research. \n", "\n", "A *job shop* refers to a collection of *machines* that process *jobs*. The jobs may be simple or complex, such as the production of a custom part, a print job at printing house, a patient treated in a hospital, or a meal produced in a fast food restaurant. The term \"machines\" can refer any device, person, or resource involved in the processing the jobs. In what follows in this notebook, a job comprises a series of tasks that requiring use of particular machines for known duration, and which must be completed in specified order. \n", "\n", "The *job shop scheduling problem* is to schedule a set of jobs on the available machines to optimize a metric of productivity. A typical metric is the *makespan* which refers to the time needed to process all jobs. \n", "\n", "At a basic level, the data required to specify a job shop scheduling problem consists of two tables. The first table is a decomposition of the jobs into a series of tasks, each task listing the job name, name of the required machine, and task duration. A second table list task pairs where the first task must be completed before the second task can be started.\n", "\n", "Real applications of job shop scheduling often include additional considerations such as time that may be required to set up machines prior to the start of the next job, or limits on the time a job can wait between processing steps. " ] }, { "cell_type": "markdown", "metadata": { "id": "N5nzDplWPHBV", "pycharm": {} }, "source": [ "## Job shop example\n", "\n", "The following example of a job shop is from from Christelle Gueret, Christian Prins, Marc Sevaux, \"Applications of Optimization with Xpress-MP,\" Dash Optimization, 2000.\n", "\n", "In this example, there are three printed paper products that must pass through color printing presses in a particular order. The given data consists of a flow sheet showing the order in which each job passes through the color presses\n", "\n", "![jobshopdiagram.jpg](https://github.com/jckantor/ND-Pyomo-Cookbook/blob/master/notebooks/figures/jobshopdiagram.jpg?raw=1)\n", "\n", "and a table of data showing, in minutes, the amount of time each job requires on each machine.\n", "\n", "| Machine | Color | Paper 1 | Paper 2 | Paper 3 |\n", "| :-----: | :---: | :-----: | :-----: | :-----: |\n", "| 1 | Blue | 45 | 20 | 12 |\n", "| 2 | Green | - | 10 | 17 |\n", "| 3 | Yellow| 10 | 34 | 28 |\n", "\n", "What is the minimum amount of time (i.e, what is the makespan) for this set of jobs?" ] }, { "cell_type": "markdown", "metadata": { "id": "6aYVOOd7PHBV", "pycharm": {} }, "source": [ "## Task decomposition\n", "\n", "The first step in the analysis is to decompose the process into a series of tasks. Each task c(job, machine) pair. Some tasks cannot start until a prerequisite task is completed.\n", "\n", "| Task (Job,Machine) | Duration | Prerequisite Task |\n", "| :----------------: | :------: | :---------------: |\n", "| (Paper 1, Blue) | 45 | - |\n", "| (Paper 1, Yellow) | 10 | (Paper 1,Blue) |\n", "| (Paper 2, Blue) | 20 | (Paper 2, Green) |\n", "| (Paper 2, Green) | 10 | - |\n", "| (Paper 2, Yellow) | 34 | (Paper 2, Blue) |\n", "| (Paper 3, Blue) | 12 | (Paper 3, Yellow) |\n", "| (Paper 3, Green) | 17 | (Paper 3, Blue) |\n", "| (Paper 3, Yellow) | 28 | - |\n", "\n", "We convert this to a JSON style representation where tasks are denoted by (Job,Machine) tuples in Python. The task data is stored in a Python dictionary indexed by (Job,Machine) tuples. The task data consists of a dictionary with duration ('dur') and (Job,Machine) pair for any prerequisite task." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "executionInfo": { "elapsed": 666, "status": "ok", "timestamp": 1603817901060, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "0k5vVxKIshoF", "pycharm": {} }, "outputs": [], "source": [ "TASKS = {\n", " (\"Paper_1\", \"Blue\"): {\"dur\": 45, \"prec\": None},\n", " (\"Paper_1\", \"Yellow\"): {\"dur\": 10, \"prec\": (\"Paper_1\", \"Blue\")},\n", " (\"Paper_2\", \"Blue\"): {\"dur\": 20, \"prec\": (\"Paper_2\", \"Green\")},\n", " (\"Paper_2\", \"Green\"): {\"dur\": 10, \"prec\": None},\n", " (\"Paper_2\", \"Yellow\"): {\"dur\": 34, \"prec\": (\"Paper_2\", \"Blue\")},\n", " (\"Paper_3\", \"Blue\"): {\"dur\": 12, \"prec\": (\"Paper_3\", \"Yellow\")},\n", " (\"Paper_3\", \"Green\"): {\"dur\": 17, \"prec\": (\"Paper_3\", \"Blue\")},\n", " (\"Paper_3\", \"Yellow\"): {\"dur\": 28, \"prec\": None},\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "F4nmNpnhshoI", "pycharm": {} }, "source": [ "## Model formulation\n", "\n", "Each task is indexed by an ordered pair $(j, m)$ where $j$ refers to a job, and $m$ refers to a machine. Associated with each task is data describing the time needed to perform the task, and a preceding task that must be completed before the index task can start.\n", "\n", "| Parameter | Description |\n", "| :-------- | :-----------|\n", "| $\\text{dur}_{j,m}$ | Duration of task $(j,m)$ | \n", "| $\\text{prec}_{j,m}$ | A task $(k,n) = \\text{prec}_{j,m}$ that must be completed before task $(j,m)$| \n", "\n", "The choice of decision variables for this problem are key to modeling. We introduce $makespan$ as the time needed to complete all tasks. $makespan$ is a candidate objective function. Variable $start_{j,m}$ denotes the time when task $(j,m)$ begins. \n", "\n", "| Decision Variables | Description |\n", "| :-------- | :-----------|\n", "| $\\text{makespan}$ | Completion of all jobs | \n", "| $\\text{start}_{j,m}$ | Start time for task $(j,m)$ |\n", "\n", "The constraints include lower bounds on the start and an upper bound on the completion of each task $(j,m)$\n", "\n", "$$\n", "\\begin{align}\n", "\\text{start}_{j,m} & \\geq 0\\\\\n", "\\text{start}_{j,m}+\\text{dur}_{j,m} & \\leq \\text{makespan}\n", "\\end{align}\n", "$$\n", "\n", "Any preceding tasks must be completed before task $(j,m)$ can start.\n", "\n", "$$\n", "\\begin{align}\n", "\\text{start}_{k,n}+\\text{dur}_{k,n}\\leq\\text{start}_{j,m}\\ \\ \\ \\ \\text{for } (k,n) =\\text{prec}_{j,m}\n", "\\end{align}\n", "$$\n", "\n", "Finally, for every task performed on machine $m$, there can be no overlap among those tasks. This leads to a set of pair-wise disjunctive constraints for each machine.\n", "\n", "$$\n", "\\begin{align}\n", "\\left[\\text{start}_{j,m}+\\text{dur}_{j,m} \\leq \\text{start}_{k,m}\\right] \\veebar \\left[\\text{start}_{k,m}+\\text{dur}_{k,m} \\leq \\text{start}_{j,m}\\right]\n", "\\end{align}\n", "$$\n", "\n", "avoids conflicts for use of the same machine. " ] }, { "cell_type": "markdown", "metadata": { "id": "HdNWs72ZPHBY", "pycharm": {} }, "source": [ "## AMPL implementation\n", "\n", "The job shop scheduling problem is implemented below in AMPL. The implementation consists of a function jobshop_model(TASKS) that accepts a dictionary of tasks and returns an AMPL model. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting jobshop_model.mod\n" ] } ], "source": [ "%%writefile jobshop_model.mod\n", "\n", "set JOBS;\n", "set MACHINES;\n", "set TASKS within {JOBS, MACHINES};\n", "set TASKORDER within {TASKS, TASKS};\n", "set DISJUNCTIONS within {JOBS, JOBS, MACHINES};\n", "\n", "param dur{TASKS};\n", "param ub;\n", "\n", "var makespan >= 0, <= ub;\n", "var start{TASKS} >= 0, <= ub;\n", "\n", "minimize minimize_makespan: makespan;\n", "\n", "s.t. finish_tasks {(j,m) in TASKS}:\n", " start[j, m] + dur[j, m] <= makespan;\n", "\n", "s.t. preceding {(j, m, k, n) in TASKORDER}:\n", " start[k, n] + dur[k, n] <= start[j, m];\n", "\n", "s.t. no_overlap {(j, k, m) in DISJUNCTIONS}:\n", " (start[j, m] + dur[j, m] <= start[k, m]\n", " or\n", " start[k, m] + dur[k, m] <= start[j, m])\n", " and not\n", " (start[j, m] + dur[j, m] <= start[k, m]\n", " and\n", " start[k, m] + dur[k, m] <= start[j, m]);" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "executionInfo": { "elapsed": 658, "status": "ok", "timestamp": 1603817901061, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "2nJ72N-rshoI", "outputId": "6786b332-59e7-4c44-83e1-1341657c5942", "pycharm": {} }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def jobshop_model(TASKS):\n", " m = AMPL()\n", " m.read(\"jobshop_model.mod\")\n", "\n", " jobs = list(set([j for (j, m) in TASKS]))\n", " machines = list(set([m for (j, m) in TASKS]))\n", " tasks = list(TASKS.keys())\n", " taskorder = [\n", " (j, m, k, n)\n", " for (j, m) in tasks\n", " for (k, n) in tasks\n", " if (k, n) == TASKS[(j, m)][\"prec\"]\n", " ]\n", " disjunctions = [\n", " (j, k, m) for (j, m) in tasks for (k, n) in tasks if m == n and j < k\n", " ]\n", " dur = {(j, m): TASKS[(j, m)][\"dur\"] for (j, m) in TASKS}\n", " ub = sum(dur[i] for i in dur)\n", "\n", " m.set[\"JOBS\"] = jobs\n", " m.set[\"MACHINES\"] = machines\n", " m.set[\"TASKS\"] = tasks\n", " m.set[\"TASKORDER\"] = taskorder\n", " m.set[\"DISJUNCTIONS\"] = disjunctions\n", "\n", " m.param[\"dur\"] = dur\n", " m.param[\"ub\"] = ub\n", "\n", " return m\n", "\n", "\n", "jobshop_model(TASKS)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 697 }, "executionInfo": { "elapsed": 1025, "status": "ok", "timestamp": 1603817901438, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "VtQDkBmBPHBc", "outputId": "649f46f0-31f8-4113-e667-858fee72a315", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HiGHS 1.5.1: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.1: optimal solution; objective 97\n", "32 simplex iterations\n", "0 branching nodes\n" ] }, { "data": { "text/plain": [ "[{'Job': 'Paper_1',\n", " 'Machine': 'Blue',\n", " 'Start': 41.99999999999993,\n", " 'Duration': 45.0,\n", " 'Finish': 86.99999999999993},\n", " {'Job': 'Paper_1',\n", " 'Machine': 'Yellow',\n", " 'Start': 86.99999999999993,\n", " 'Duration': 10.0,\n", " 'Finish': 96.99999999999993},\n", " {'Job': 'Paper_2',\n", " 'Machine': 'Blue',\n", " 'Start': 9.999999999999972,\n", " 'Duration': 20.0,\n", " 'Finish': 29.99999999999997},\n", " {'Job': 'Paper_2',\n", " 'Machine': 'Green',\n", " 'Start': 0.0,\n", " 'Duration': 10.0,\n", " 'Finish': 10.0},\n", " {'Job': 'Paper_2',\n", " 'Machine': 'Yellow',\n", " 'Start': 29.99999999999997,\n", " 'Duration': 34.0,\n", " 'Finish': 63.99999999999997},\n", " {'Job': 'Paper_3',\n", " 'Machine': 'Blue',\n", " 'Start': 29.99999999999997,\n", " 'Duration': 12.0,\n", " 'Finish': 41.99999999999997},\n", " {'Job': 'Paper_3',\n", " 'Machine': 'Green',\n", " 'Start': 79.99999999999997,\n", " 'Duration': 17.0,\n", " 'Finish': 96.99999999999997},\n", " {'Job': 'Paper_3',\n", " 'Machine': 'Yellow',\n", " 'Start': 1.999999999999972,\n", " 'Duration': 28.0,\n", " 'Finish': 29.99999999999997}]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def jobshop_solve(model):\n", " model.option[\"solver\"] = SOLVER\n", " model.solve()\n", "\n", " start = model.var[\"start\"].to_dict()\n", " dur = model.param[\"dur\"].to_dict()\n", " tasks = model.set[\"TASKS\"].to_dict()\n", "\n", " results = [\n", " {\n", " \"Job\": j,\n", " \"Machine\": m,\n", " \"Start\": start[j, m],\n", " \"Duration\": dur[j, m],\n", " \"Finish\": start[j, m] + dur[j, m],\n", " }\n", " for j, m in tasks\n", " ]\n", " return results\n", "\n", "\n", "def jobshop(TASKS):\n", " return jobshop_solve(jobshop_model(TASKS))\n", "\n", "\n", "results = jobshop(TASKS)\n", "results" ] }, { "cell_type": "markdown", "metadata": { "id": "7uDCIyqpshoO", "pycharm": {} }, "source": [ "## Printing schedules" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 425 }, "executionInfo": { "elapsed": 1015, "status": "ok", "timestamp": 1603817901439, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "ZmtO_2EeshoO", "outputId": "fecdce9c-ddca-4aad-d7fe-c3d8fcebde8a", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Schedule by Job\n", " Start Duration Finish\n", "Job Machine \n", "Paper_1 Blue 42.0 45.0 87.0\n", " Yellow 87.0 10.0 97.0\n", "Paper_2 Green 0.0 10.0 10.0\n", " Blue 10.0 20.0 30.0\n", " Yellow 30.0 34.0 64.0\n", "Paper_3 Yellow 2.0 28.0 30.0\n", " Blue 30.0 12.0 42.0\n", " Green 80.0 17.0 97.0\n", "\n", "Schedule by Machine\n", " Start Duration Finish\n", "Machine Job \n", "Blue Paper_2 10.0 20.0 30.0\n", " Paper_3 30.0 12.0 42.0\n", " Paper_1 42.0 45.0 87.0\n", "Green Paper_2 0.0 10.0 10.0\n", " Paper_3 80.0 17.0 97.0\n", "Yellow Paper_3 2.0 28.0 30.0\n", " Paper_2 30.0 34.0 64.0\n", " Paper_1 87.0 10.0 97.0\n" ] } ], "source": [ "import pandas as pd\n", "\n", "schedule = pd.DataFrame(results)\n", "\n", "print(\"\\nSchedule by Job\")\n", "print(schedule.sort_values(by=[\"Job\", \"Start\"]).set_index([\"Job\", \"Machine\"]))\n", "\n", "print(\"\\nSchedule by Machine\")\n", "print(schedule.sort_values(by=[\"Machine\", \"Start\"]).set_index([\"Machine\", \"Job\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "EBvet1ACshoS", "pycharm": {} }, "source": [ "## Visualizing Results with Gantt Charts\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 477 }, "executionInfo": { "elapsed": 1586, "status": "ok", "timestamp": 1603817902020, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "RtQ5NWPTshoU", "outputId": "bab2addd-0b90-45ef-d5eb-e65eb43d823f", "pycharm": {} }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "\n", "def visualize(results):\n", " schedule = pd.DataFrame(results)\n", " JOBS = sorted(list(schedule[\"Job\"].unique()))\n", " MACHINES = sorted(list(schedule[\"Machine\"].unique()))\n", " makespan = schedule[\"Finish\"].max()\n", "\n", " bar_style = {\"alpha\": 1.0, \"lw\": 25, \"solid_capstyle\": \"butt\"}\n", " text_style = {\"color\": \"white\", \"weight\": \"bold\", \"ha\": \"center\", \"va\": \"center\"}\n", " colors = mpl.cm.Dark2.colors\n", "\n", " schedule.sort_values(by=[\"Job\", \"Start\"])\n", " schedule.set_index([\"Job\", \"Machine\"], inplace=True)\n", "\n", " fig, ax = plt.subplots(2, 1, figsize=(12, 5 + (len(JOBS) + len(MACHINES)) / 4))\n", "\n", " for jdx, j in enumerate(JOBS, 1):\n", " for mdx, m in enumerate(MACHINES, 1):\n", " if (j, m) in schedule.index:\n", " xs = schedule.loc[(j, m), \"Start\"]\n", " xf = schedule.loc[(j, m), \"Finish\"]\n", " ax[0].plot([xs, xf], [jdx] * 2, c=colors[mdx % 7], **bar_style)\n", " ax[0].text((xs + xf) / 2, jdx, m, **text_style)\n", " ax[1].plot([xs, xf], [mdx] * 2, c=colors[jdx % 7], **bar_style)\n", " ax[1].text((xs + xf) / 2, mdx, j, **text_style)\n", "\n", " ax[0].set_title(\"Job Schedule\")\n", " ax[0].set_ylabel(\"Job\")\n", " ax[1].set_title(\"Machine Schedule\")\n", " ax[1].set_ylabel(\"Machine\")\n", "\n", " for idx, s in enumerate([JOBS, MACHINES]):\n", " ax[idx].set_ylim(0.5, len(s) + 0.5)\n", " ax[idx].set_yticks(range(1, 1 + len(s)))\n", " ax[idx].set_yticklabels(s)\n", " ax[idx].text(\n", " makespan,\n", " ax[idx].get_ylim()[0] - 0.2,\n", " \"{0:0.1f}\".format(makespan),\n", " ha=\"center\",\n", " va=\"top\",\n", " )\n", " ax[idx].plot([makespan] * 2, ax[idx].get_ylim(), \"r--\")\n", " ax[idx].set_xlabel(\"Time\")\n", " ax[idx].grid(True)\n", "\n", " fig.tight_layout()\n", "\n", "\n", "visualize(results)" ] }, { "cell_type": "markdown", "metadata": { "id": "JMWyAp2MshoW", "pycharm": {} }, "source": [ "## Application to the scheduling of batch processes\n", "\n", "We will now turn our attention to the application of the job shop scheduling problem to the short term scheduling of batch processes. We illustrate these techniques using Example II from Dunn (2013).\n", "\n", "![flowshop-Dunn-2013.png](https://github.com/jckantor/ND-Pyomo-Cookbook/blob/master/notebooks/figures/flowshop-Dunn-2013.png?raw=1)\n", "\n", "| Recipe | Mixer | Reactor | Separator | Packaging |\n", "| :-----: | :---: | :-----: | :-------: | :-------: |\n", "| A | 1.0 | 5.0 | 4.0 | 1.5 |\n", "| B | - | - | 4.5 | 1.0 |\n", "| C | - | 3.0 | 5.0 | 1.5 |" ] }, { "cell_type": "markdown", "metadata": { "id": "FllAPz4KshoX", "pycharm": {} }, "source": [ "### Single product strategies\n", "\n", "Before going further, we create a function to streamline the generation of the TASKS dictionary." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 459 }, "executionInfo": { "elapsed": 2006, "status": "ok", "timestamp": 1603817902449, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "xfurTCWyshoY", "outputId": "d027a1a6-e465-426e-b800-4c5ff3522443", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution determined by presolve;\n", "objective minimize_makespan = 11.5.\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def recipe_to_tasks(jobs, machines, durations):\n", " TASKS = {}\n", " for j in jobs:\n", " prec = (None, None)\n", " for m, d in zip(machines, durations):\n", " task = (j, m)\n", " if prec == (None, None):\n", " TASKS.update({(j, m): {\"dur\": d, \"prec\": None}})\n", " else:\n", " TASKS.update({(j, m): {\"dur\": d, \"prec\": prec}})\n", " prec = task\n", " return TASKS\n", "\n", "\n", "recipeA = recipe_to_tasks(\n", " \"A\", [\"Mixer\", \"Reactor\", \"Separator\", \"Packaging\"], [1, 5, 4, 1.5]\n", ")\n", "visualize(jobshop(recipeA))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "executionInfo": { "elapsed": 2441, "status": "ok", "timestamp": 1603817902899, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "QndOwo19shoc", "outputId": "65bcc252-7e07-409c-ab29-23761ff73567", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution determined by presolve;\n", "objective minimize_makespan = 5.5.\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "recipeB = recipe_to_tasks(\"B\", [\"Separator\", \"Packaging\"], [4.5, 1])\n", "visualize(jobshop(recipeB))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 441 }, "executionInfo": { "elapsed": 2809, "status": "ok", "timestamp": 1603817903280, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "VR2QKxzRshof", "outputId": "a7ed824a-4176-462a-e613-7c3b46f6ea7c", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution determined by presolve;\n", "objective minimize_makespan = 9.5.\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "recipeC = recipe_to_tasks(\"C\", [\"Separator\", \"Reactor\", \"Packaging\"], [5, 3, 1.5])\n", "visualize(jobshop(recipeC))" ] }, { "cell_type": "markdown", "metadata": { "id": "s1U47NNXshoj", "pycharm": {} }, "source": [ "### Multiple Overlapping tasks\n", "\n", "Let's now consider an optimal scheduling problem where we are wish to make two batches of Product A." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 530 }, "executionInfo": { "elapsed": 4166, "status": "ok", "timestamp": 1603817904648, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "EGU9wmLoshoj", "outputId": "04c36086-113c-42aa-de15-ad126e748291", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HiGHS 1.5.1: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.1: optimal solution; objective 26.5\n", "3411 simplex iterations\n", "79 branching nodes\n", "absmipgap=1e-06, relmipgap=3.77358e-08\n", "Makespan = 26.5\n" ] }, { "data": { "image/png": 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4hkBC6ghPaS45j/UPRXgiIiLSCPhq3Lg252GMs2POTMR0XjtMWYkUjpkR6tDqh8UILm+ooxBg3Bl/5px21wNwuGwXLq+TpKjmdG8xghV7Pwq7pJTJaMHjdYU0BqPBjNfnbtB9uDxOcoo3YjbZaBbXlq7NzqVT08E8vWAce4rWNui+G8qH720IdQhhR0mpBrB06VIGDBjAiBEj+PTTT4/bprCwkG7dunHgwAGKi4uJj48PbpAiv5LP68FgNBE/bJI/KWUwEj/0j/jcNRjM1kA7c3IGWc/sAiDnyaE496wm4/E1WJIzKV0yg8PTbsBgiyLz8bVYUlpR9s0b5L4yEQB7Vh+SxjyIvU1fDBY7NQc2UfTJk1Ssei/Qf9sZHgDyZ99LzJmXBe8FEBERkaDw5jsomvAWAHFPjCJiTCcsbVIwxNnxlVZjiLYSPXkA9nNaY0yJxltYSfX/tlL+whKo9n9YtvbNIPrmfpgyEzDG2vE53bi35lPx8jJqluwO7MsQaSH6lv7YzmmDqWkMPkcNrvW5FN/6ISmfXI+pWZy/vz4tSdv0fwAUXTuLmpU5GJvGEHPrQKz9MzHG2fEWVuJctIPyF77GV1rtj/+YSivn4t1EXtUDY2o0hzs/E8yXVH5Cr5ajAfhk/XPM2fBsYH12ci/KqwsCjw0YGNJ2IgOyJ5Aak0GNx8mW3K95b80TFDpygNqVSVMWjufSHg/SNDabw+W7mbXqIbbnLwegeXxHLu3+AE3j2hBpjcPr83CodDtfbJvO8j0fBPb57wl7AXhvzd/ISupBh7SBrM6Zy2vL72biWVPISu5JnD0Fs9FKaXUea/cv4KN1z1DtruDaM5+hX9aldfqas/5ZPtnwHBaTjVGdbqVXy9EkRqbjdFeyPX85H62bwsHSrXWO54Uvr+GS7g/QJCaL5xZdyba8ZfX7i/iR0qo8nlowFoDO6UOZPHg6JqOFPplj6N5iBF2bnUuCvTlWsx1HRQ3btuUz75MtlJc5A300bxHHOcPakJmViM1morS0mqVf72Xxl7uOu8/RF3Zg0JBsamo8zJi2kh3bCrjsim5kZiYSE2PDZDZSXlbNxg2HmT93K07n0cTc8JFt6dsvE6PJwOpVB3BWuxg6rA0A99zxCeAfvpfdOolVK3J45+21JCREcN+D5wAwe+YaunZrSnabJLhrFzz+eIO8ruFGc0o1gGnTpjF58mQWL17MwYMHj9vmhhtuoGvXrkGOTOS3c5fkUpO3k+juYzAnNCO6+4VYkjMoX/nez27nrS4n9+WJ+Lwe4gZOJLLTuaRc9iSWlFa48neT98ZkAOyt+9Hiz18R1W0kvpoq3AV7sGf2IH3SO8T0u7pOv0kXP4olpVWDHKuIiIiEF29ZNT5HDViMJL52OVFX98SYGIl7ZyHGeDtRE3uR8NLFgfbm1slYujbF53Dh3l4ABgPWns1JmDoWc7sUf6MjfV3XG3PLeLx5FXhLq7H2z8RgNeHanIe3yD/UxlvhpGbtQWrWHsRb4cSYGEnSzCuJGNMJY4wN995ijEmRRE7oTuJrl4PVVCt+yxnpRN8xEJ/Dia+0Kmivm/w8g8H/sbhj00F0SR9KjD0ZgJ0Fq8ir2BNod3mvxxjf82Gaxbcjr3wvPp+Hni3P595h7xNjS6rT76TB0zEbrfh8PprHd2DS4OnERaQCkBTVnLapZ+HyODlUuh23p4bMpG5c3/c5OqcPrdPXhV3uon2TfhQ6cgJVUt2aDSPKGkd+xV6KKw+RFNWcoe2u45oznwYgv2If+eV7A33sKljNroLVFFfmAnDzoGmM6jSJ5OiW5Ffsw2Q0c0bz87h32Ps0icmuE8MfB76C1RRBceWhX/My16tOTQcTH9GEUkcxBQUOomNs9Ordgmuv7x1ok5GZwM239qNTlzRsNhMF+Q7sNguZWYnH7XP4iLb+hJTTzfRXVrBjmz8h2alzGhERFgoLHZSWVJGQGMmAQa249PKjn+f7Dczk3OFtiYq24nS66XpGU/oPOrnPKOMu60qTtBi8Hh+0aoXvlf9ARHDmugolVUrVs4qKCmbPns2qVavIzc1lxowZ/PnPf67V5v/9v/9HSUkJDz74IPPmzQtRpCK/lo+ShS+ROmEKcUP/QET2WQCUfD6V2L4TfnbLqm1LKJ73DInn30vaja9jiknB5/Vw6OVr8VaXA5A87lEMZiuODQs48I/zweshZcIUEs67neRxj1H+7Ru1+nTl76Ji9cckjb63YQ5XREREQsKYEkXi21cGhu95S6oo/ct8cHuxj+mEpUMTfDVuCsbOwLO3BHO7FJI/mIjtrAysZ7WkZtk+qhdup+rDDfjK/ZUThlgbKZ/fhDHahn14Wyq25mMf2QFLpzQAyp75kspXVwJgbp2Er9pFya0fBiqd3JsO15pTKnpSf0xNYvB5vBRe8RbuzXnYzmlNwj/HYmmbQsT5Haj64OhwHYPVTNFN7/qrtIyGIL6a8nO+2v4GF3S5g6zkHkwaPB2A3LIdLN/zEZ9t/jdur5OkqBYMan0lANOX3sGyPe9jM0fy8KiFJEalM6TtRD5eP6VWv/9d/RhLds6kaWwb/jJyHnZLFEPbXscHa59id+H33PNRn0Alltlo48FR82kS04reLS9gw8EvavVVUJHD0wvGUukqCyTRpiwcX2to4ZgudzOq82TOaD4cs9HG3I0vUFx5MFDpdKTqCKBtal86pg0E4N3vH2Ph1leJj0jjkfMXYrdEM7LTzcxYdletGBZuncYHa58CjibyGlJcRCr3DvsgMHwPwON1sWLvx3y9czaHSreRXDGCmk2j6XNmCy65vBstM+JJTIqkqLCS80a1w2w2UVlZw9TnvqEg34HBAGlNY+rsa9CQbPoPzPQnpP6zkp07CgPP/Wvqtxw6WB54fN7IdpwzvA2duqRhNhtxu72cPcSfxNuzu4h/TV2KyWzktjsHktok+oSPd9OGXN58bTVpTWO4857BGIwGoPG/TygpVc/eeecd2rdvT7t27bjqqqu4/fbbue+++zAY/CfTpk2bePTRR1m+fDm7dh2/ZPDHnE4nTufREsSyMv+k0S6XC5crtGOJf60jcZ+q8Z+OTCYTRqMRfD7KlkwneewjxJ87CVNELNW7V1G988TKdwvef4jILudhb3kG4J8UvXr7N4Hn7Vl9AIjqPIy2r9bU2taS1AJzfDrukqMViGXfvI7PaAmbc0nntjRmOr+lsQrXc9vr9YQ6hJAyWM1Yu6UHHrt3FuL6/gAAli5NA21S5v2+zraWbunULNuHwWIi9m8jsZyRjjE+AoPp6AdpY6r/w6Klq78vn9NN5YxVR/d3zIfSn2Lp7E9mefYU4d6cB4Bz4Q68lTUYI62YO6XBMUkp967Co8MGvb5ffhEaMZ8vfI7/yGTm/VpdStvUM4mwxpIW25oxXe8iJbolry2/m4zELhh/SMRc1/dZruv7bK0+WiV3r9Pvyr0fA3CobDsHSrbSMrEzzeLaA+DDx6XdH6Bdaj9i7EmYjEc/msdHNKnT19Ld71Lp8n8O9Pn8c5G1TxvA9X2fJyU6A6vZHmhrMlqIsSf+bEVTZtLRKp8Vez4CoKQql+35K+mSPoSMxLqjehZufTWwfCSGhmQx2chK7oHX66GsuoB9xRuYt+lF9hSu4czMsUw8awppMW2wWey1touNs1NUWEnLlvEArF+bS0G+44e4qZVgOqL/wEwAZr7xfa2EFECbtilMuKo7SUlRWI6pfjSZjERFW6muchP/w937Nq7Pxev14a3xsGXT4ZNKSn3/nf/9Le9wRWCdx+PB6/UGPov5fD7c7qNDBi0WCwButzus/k/Bif9NVVKqnk2bNo2rrroKgBEjRlBaWspXX33F2WefjdPpZMKECfz973+nZcuWJ5yUeuKJJ3jkkUfqrP/ss8+IjIys1/iDbcGCBaEOQU5Q//79SU5Oprq6Gm9lKWVL3yJ+yE0AlHz+4gn3Y7RFYYpODjy2pNYtDQZwFe3HXby/7hOm2m9bntI8dhZUUjB37gnHEAw6t6Ux0/ktjVW4ndvNthfQPNRBhJDnQCn5w1/G2i+ThKljsfZsTuyjwymZ9GGgzZHJ0H/M+8NcTgn/Goc5IwGfy4N7ewE+pxtLh1QMVjMG43EqPRr4Q523UHfcOqKoqBCiQh3FUWv2z2fN/vkYMNAysQvXnPk0zeM7cEbz4by2vHbbfcUbcXuctdYVOQ6c1P6u7/scHdMG4vV5OVS6HafbQdO4NkRYYo5bhXTs3FYAfTIu4tLuDwBQUnmYAyWbibYlkhLjv8Oe0WCq08dvVVadX+99/pyCihzunzOgzvrs5F5MPOsfGA1GHNUVHD5Ygs1mokmavwLKaDj56iJntRub3cygIVls25aP+4ebEHTv0YzRYzoCUFZazaFDZURFWUlKjvrV+/opVVX+ZJPBdfSL+R1bt7Jl+3Z69+5Neno65eXlLFq0CICYmBiGDvUP9Vy3bh05OTn1Fkt9qKw8sfc7JaXq0datW1mxYgUffOCfmM5sNjN+/HimTZvG2WefzX333UeHDh0CSasTdd9993HnnXcGHpeVldGiRQuGDx9ObGxsvR5DsLhcLhYsWMCwYcMC2V0JbyaT/w+b3e7/JqLk8xeJH3IT7rI8ypfPOuF+mlz7/7AkNsd5cDOWlFbE9L6Ein5XB4blVe9eSWT7s3EX7mX/08PxufwXleaEZtgye+Iu3PejHn20adOGPqNG/faDrAc6t6Ux0/ktjVW4ntuVe5dStXD5LzdszHxQ880eKt/+nqiJvbEPbYO5cxruDYeA7mA0UvbogkCVElYTtsHZ1CzbiyHOjjkjAYCKqd/geGU5pvRYkj+9odYuXOsOwRXdMdjMRF7Ti8rX/NVSpqxEPDkl4PLiq/Z/42+IqH1+uDbkYhuUhSkzEXOH1MDwPWOk/+Yv7o25Pzqe8KpkCKXExLpzMIXKmC53813OXPaXbMKHj71F6zhctpvm8R2ocvmravYVrcfr82I0GFm66798sW16YPvWKb0D7Y7VK2M0X++cRVpsa5rFtwPgQOkWALKS/JVVX+98m7dW/plIaxwPjVxAhKXu0DLwV1YdK+uHyqwqVzn3zxmA21vDFb0eZ3BM7TlYa9xH5y6zmiKo8fgf7ylcF1jfJ3NMYPhemxT/nEx7i9YRrloldw9Urf3z3Ucp+m4QZ5+TzajRHWq127evhNZtkunSNY0vv9hBYYE/SZLWNIbcQ7V/X7NmrmH8Fd3Iyk7iqmt78vqrq/B6fbTMjAegutrFE499gcfjZewlnembfDSj6nS6KS6uIiEhgg4dm7Dkq92YTAbad6xb8XYijMdUybbOyiKrTRv/iBX8iahRP3zuMR6TWO/atStdunT5VftrKEdGeP0SJaXq0bRp03C73aSnHy0z9vl82Gw2pk6dyhdffMH69et59913A88BJCcnc//99x+3GgrAZrNhs9nqrLdYLGF14fRrNIZjOO388G1AzYGN7LglGTxufO6aX9jIL6bvlcSceRlel5NDL11OVNeRpFz2JKlXPU/Vli9xF+VQ+P7DRNyzgIg2/cl6/gCu/N2YYlIwx6dTtW0xju8/rtOv0WgKu/NI57Y0Zjq/pbEKt3PbaKz/SodTlWP6SiKv6I7Baib6xrMouWsOkdf0wtI+laR3rsa9qxCD2YQpPRaDzUz+uf/Gc7AMz6EyTE1jiZ7UH/v5HTA1icbn8daapaV63mZcV/fA0imN2HuHEHlld3B7MbWIJ6/fVHwuJ+7dRYB/2GDShxPxVbkomjibypnfE3FJV0yp0STNvBL3vmLMmf5JlF3b8qn6dPNxjkaAwPQm4aB/9nhGdZ5MeXUhRZUHibElkRjl/0y3Yq9/aFuBI4evd77NoNZXMr7nwwxtdz1Ot4OkyGZEWGOZsewuDpRsqdXvJWfczzntbiApqjkmowWnu5JF22YAsL9kC61TejEg63Jap/QmLqLJSSUtj8wlFWGJ4fELluD21hw3oZVbvjOw/PD5n1Nalce73/+VbXlL2ZS7hI5pA7mk+18YkH0FCZFp2C3RVLsqmLfxpZN6DYPpwDHzaE2+5EEqhvmIjq77eXn+3K1k3pJAZJSVu+4dTH6eg5hYG3t2F/P6q6tqtT24v5TXp3/H9b/vQ8dOTbj08m7MnrkmMNTPbrfwpweG4vZ4sNvr/p34atFOLrq4M1mtk7j3gaGYzUZs1t/+Hm4yGjEd83fJYDAc9++U2Rx+qZ0T/Xuqu+/VE7fbzeuvv86UKVNYs2ZN4Gft2rWkp6fz9ttv895777F27drAc//5z38AWLJkCbfcckuIj0Dk5HkdxYEJyn+JObEFqVe/AEDRnL9Ss38DxfOmULVjKabIONJunAEGA1XblpDzxNk41s4Dnw9rekfwuKj47n2K5/2jAY9GREREwpU330HVx5sAsA1tjTkjnqJrZ+F44zu8ueWYMxMxxNpwbcil/LnFeH4YJldy+0fUrDsEHi8Gk4GSez7FV/yju965vBRNnI1j+krcOSWYmsRgjI+gZulefDX+ioWq99dTPX8r3rJqLG1TsHZLx2Ay4C2qpHDCm1R9tBFvudM/IXthJZWz1lB07SyoOb3nBTtVfLRuCmv2z6fa7SAtNpsYexK5ZTuYs/5ZPl53dPLymSvv553Vj7C/ZDPxEakkRTajwLGfBVteYVte3flVX/hqIi6PE6PBxIGSLby4+AZKqg4D8Nqyu9hy+FtcXidWU0Sg3xP1zc7ZLNjyCuXVhdgtUWzLW8ac9XWvlQ+UbOGTDc9TWpVHUlRzspJ7EGn1j7Z5afENzN04lcKKHJrEZOL1elizfz5PLbiYw8cks8LN5tyveW/N3yiuzMVispB/2MEH766v027vnmJeeuFbNm3Ixen0kJIaTY3Tw55dRcftd8e2Av47ay1er4+evZtz4dhOrFy+j8WLdlJR4cRmN7FrRyGfzdtaZ9tvl+zh88+24XDUYLeb2bg+lxXL/cPpXHof+FkGX7jNhnWK+vDDDxk/fjx5eXnExcXVeu7ee+/liy++YOXKlbXWf/nllwwZMoTi4mLi4+NPeF9lZWXExcVRWlp6Sg/fmzt3LqNGjQqrbyTllxV88AhFHz0a6jBqSRzzIMljHwp1GIDObWncdH5LYxWu53b51G9wvPRtqMMQaRBf33WQBZHvhzqMete31SWBu93d9HZGiKNp/FIqRlKzaXSow8BuN2MyG3FU+EeQmM1GJt8xgKbpsezdU8yLz3/zCz0cZXFV89cXL/U/qKiAqDCafO0knGjeIvxqvE5R06ZN49xzz62TkAIYN24cTz/9NOvWraNr17p3MRARERERERGRU1NCYiSTbu9Pzr4SqqtcNGseT1y8HY/Hy2f/q1tZJUcpKVVP5syZ85PP9enT57i3Zzz77LPD7raNIiIiIiIiInLiHI4adu8qIr1ZLBERFqqr3GzedJhFC3f+5HBB8VNSSkRERERERKQeLN39Lkt3vxvqMCTIykqr+c+/TvO7pf5KSkqJiIiIiIiIiIQBt9nKlKunclb/DPpHRIQ6nAanu++JiIiIiIiIiIQBn8HI4aQMHBltwNj4UzaN/whFRERERERERCTsKCklIiIiIiIiIhIGTB4Xw5bOJPvNf0JNTajDaXBKSomIiIiIiIiIhAGj18Ow5W+T/daL4HKFOpwGp6SUiIiIiIiIiIgEnZJSIiIiIiIiIiISdEpKiYiIiIiIiIhI0CkpJSIiIiIiIiIiQWcOdQAicmoxRSdiSc0OdRi1mKITQx2CiIhIvTPG2TG1iA91GHKK8+Gj0lFJZFQkBgyhDicgylZFSnRGqMOQU5jP58PqiCQ6KQKDIXzO7d/KUtN4juVEKCklIiclYdhkEoZNDnUYIiIijV7U1T2JurpnqMOQU5zL5WLp3LmMGjUKi8US6nACRgIjeTDUYcgpzOVyMXfuXEZdOyiszu3fzOGAR0MdRPAoKSUiIiIiIiIiEg7sdlix4uhyI6eklIiIiIiIiIhIODCZoHfvUEcRNJroXEREREREREREgk6VUiIiIiIiIiIi4aCmBp5/3r98221gtYY2ngampJSIiIiIiIiISDhwueCee/zLN9/c6JNSGr4nIiIiIiIiIiJBp6SUiIiIiIiIiIgEnZJSIiIiIiIiIiISdEpKiYiIiIiIiIhI0CkpJSIiIiIiIiIiQaeklIiIiIiIiIiIBJ051AGIiIiIiIiIiAhgt8OiRUeXGzklpUREREREREREwoHJBGefHeoogkbD90REREREREREJOhUKSUiIiIiIiIiEg5cLnj5Zf/yjTeCxRLaeBqYklIiIiIiIiIiIuGgpgYmTfIvT5zY6JNSGr4nIiIiIiIiIiJBp6SUiIiIiIiIiIgEnZJSIiIiIiIiIiISdEpKiYiIiIiIiIhI0CkpJSIiIiIiIiIiQaeklIiIiIiIiIiIBJ051AGIiIiIiIiIiAhgs8EnnxxdbuSUlBIRERERERERCQdmM5x/fqijCBoN3xMRERERERERkaBTpZSIiIiIiIiISDhwueCtt/zLV14JFkto42lgSkqJiIiIiIiIiISDmhq47jr/8qWXNvqklIbviYiIiIiIiIhI0CkpJSIiIiIiIiIiQaeklIiIiIiIiIiIBJ3mlBIJY8UL/knJgn+GOoxa4odNJmHY5FCHISICgOON76h8c3Wow5BTnA8f3RyVFL80HQOGUIcTsOrqQ3wT93mow5BTnM/nw5bfm81PRWMwhM/5XV/6D2zFgEGtQh2GiPxKSkqJhDFPRRGuvJ2hDqMWT0VRqEMQEQnwllbjySkJdRjSCNgBb1FpqMOoxeEsJr9ib6jDkEYgxdeJisKqUIfRICora0Idgoj8Bhq+JyIiIiIiIiIiQadKKRERERERERGRcGCzwTvvHF1u5JSUEhEREREREREJB2YzXHppqKMIGg3fExERERERERGRoFOllIiIiIiIiIhIOHC74YMP/Mtjx/orpxqxxn10IiIiIiIiIiKnCqcTLrvMv1xR0eiTUhq+JyIiIiIiIiIiQaeklIiIiIiIiIiIBJ2SUiKNgDk5g7YzPLSd4SGi/eBQhyMiIiIijUBWdhJPPzuap58dTUJCRFD2edMtfXn62dFcNqFbUPYnIqHVuAcnipziksc+REzPi9j7YI/AOmNUIlnP7sNo9V8YFH36FMWfvUDVzmUAeKvKQhKriIgEV+KM8Vj7tAw89rm9eEuqcH23n/JnvsJzoDToMcX9dSQRYztTs2IfRRNnB33/0nBMRgvD299In8yxJEU1w+vzUF5dyIGSLXyy4Tn2l2wOdYi/Wt9WlzDxrCkA3PR2RoijOTF3Dp1FuyZ9A4+9Xg+l1fnkFG9g3qaX2FXwXQij+20O55ZjNhspLKgMdSgiEgSqlGoAS5cuxWQycf7559daX1hYyIgRI0hPT8dms9GiRQsmTZpEWZmSCPLTbC27EdF2YOBx3OAbAgmpIzylueQ81p+cx/rj3Pt9sEMUEZEQ8tW4qVl7EM/+EkzJUdjPa0f8S2NDHVb9sehyNRyMO+PPXNTtHtLj2lBSmUuhYz8x9iS6txhBakyrUIdXh8loCXUIGA0N//2/y+NkV8Fq9pduIdaeRNdm53L3ObPJTDx1q4w+fG8DLz7/DQsXbA91KCISBKqUagDTpk1j8uTJTJs2jYMHD5Keng6A0WhkzJgxPP7446SkpLBjxw5uueUWioqKmDlzZoijlnDk83owGE3ED5tE1bYlYDASP/SP+Nw1GMzWQDtzcgZZz+wCIOfJoTj3rCbj8TVYkjMpXTKDw9NuwGCLIvPxtVhSWlH2zRvkvjIRAHtWH5LGPIi9TV8MFjs1BzZR9MmTVKx6L9B/2xkeAPJn30vMmZcF7wUQEZFf5M13UDThLQDinhhFxJhOWNqkYIiz4yutxhBtJXryAOzntMaYEo23sJLq/22l/IUlUO0GwNo3g+ib+2HKTMAYa8fndOPemk/Fy8uoWbI7sC9DpIXoW/pjO6cNpqYx+Bw1uNbnUnzrh6R8cj2mZnH+/vq0JG3T/wFQdO0salbmYGwaQ8ytA7H2z8QYZ8dbWIlz0Q7KX/gaX2m1P/5jKq2ci3cTeVUPjKnRHO78TDBfUjmOXi1HA/DJ+ueYs+HZwPrs5F6UVxcEHhswMKTtRAZkTyA1JoMaj5MtuV/z3ponKHTkALUrk6YsHM+lPR6kaWw2h8t3M2vVQ2zPXw5A8/iOXNr9AZrGtSHSGofX5+FQ6Xa+2Dad5Xs+COzz3xP2AvDemr+RldSDDmkDWZ0zl9eW383Es6aQldyTOHsKZqOV0uo81u5fwEfrnqHaXcG1Zz5Dv6xL6/Q1Z/2zfLLhOSwmG6M63UqvlqNJjEzH6a5ke/5yPlo3hYOlW+sczwtfXsMl3R+gSUwWzy26km15y+r3F/EjpVV5PLXAn4TunD6UyYOnYzJa6JM5hu4tRtC12bnERzTBZo7GUe5i27Z85n2yhfIyZ6CP5i3iOGdYGzKzErHZTJSWVrP0670s/nLXcfc5+sIODBqSTU2NhxnTVrJjWwGXXdGNzMxEYmJsmMxGysuq2bjhMPPnbsXpdAe2HT6yLX37ZWI0GVi96gDOahdDh7UB4J47PgH8w/eyWyexakUO77y9loSECO578BwAZs9cQ9duTcluk0xFuVOJK5FGQF891bOKigpmz57NH//4R84//3xmzJgReC4hIYE//vGP9OrVi4yMDM455xxuvvlmlixZErqAJay5S3KpydtJdPcxmBOaEd39QizJGZSvfO9nt/NWl5P78kR8Xg9xAycS2elcUi57EktKK1z5u8l7YzIA9tb9aPHnr4jqNhJfTRXugj3YM3uQPukdYvpdXaffpIsfxZISft+GiohIbd6yanyOGrAYSXztcqKu7okxMRL3zkKM8XaiJvYi4aWLA+3NrZOxdG2Kz+HCvb0ADAasPZuTMHUs5nYp/kZH+rquN+aW8XjzKvCWVmPtn4nBasK1OQ9vkX+4jbfCSc3ag9SsPYi3wokxMZKkmVcSMaYTxhgb7r3FGJMiiZzQncTXLgerqVb8ljPSib5jID6HE19pVdBeN/lpBoP/Y0PHpoPokj6UGHsyADsLVpFXsSfQ7vJejzG+58M0i29HXvlefD4PPVuez73D3ifGllSn30mDp2M2WvH5fDSP78CkwdOJi0gFICmqOW1Tz8LlcXKodDtuTw2ZSd24vu9zdE4fWqevC7vcRfsm/Sh05ODxugDo1mwYUdY48iv2Ulx5iKSo5gxtdx3XnPk0APkV+8gv3xvoY1fBanYVrKa4MheAmwdNY1SnSSRHtyS/Yh8mo5kzmp/HvcPep0lMdp0Y/jjwFaymCIorD/2al7ledWo6mPiIJhRXHqKwNI/oGBu9erfg2ut7B9pkZCZw86396NQlDZvNREG+A7vNQmZW4nH7HD6irT8h5XQz/ZUV7NjmT0h26pxGRISFwkIHpSVVJCRGMmBQKy69vGtg234DMzl3eFuioq04nW66ntGU/oNO7rpy3GVdaZIWg8fjJTEpknGXdSUyyvrLG4qcSqxWmD7d/2Nt/Oe3KqXq2TvvvEP79u1p164dV111Fbfffjv33XcfBoOhTtuDBw/y/vvvM3iwJqaWn+KjZOFLpE6YQtzQPxCRfRYAJZ9PJbbvhJ/dsmrbEornPUPi+feSduPrmGJS8Hk9HHr5WrzV5QAkj3sUg9mKY8MCDvzjfPB6SJkwhYTzbid53GOUf/tGrT5d+buoWP0xSaPvbZjDFRGRk2ZMiSLx7SsxxtkxZybiLami9C/zwe3FPqYTlg5N8NW4KRg7A8/eEsztUkj+YCK2szKwntWSmmX7qF64naoPN+Ar91dPGGJtpHx+E8ZoG/bhbanYmo99ZAcsndIAKHvmSypfXQmAuXUSvmoXJbd+GKh0cm86XGtOqehJ/TE1icHn8VJ4xVu4N+dhO6c1Cf8ci6VtChHnd6Dqgw2B9garmaKb3vVXaRnrXkNJ8H21/Q0u6HIHWck9mDR4OgC5ZTtYvucjPtv8b9xeJ0lRLRjU+koApi+9g2V73sdmjuThUQtJjEpnSNuJfLx+Sq1+/7v6MZbsnEnT2Db8ZeQ87JYohra9jg/WPsXuwu+556M+gUoss9HGg6Pm0ySmFb1bXsCGg1/U6qugIoenF4yl0lUWSKJNWTi+1nxXY7rczajOkzmj+XDMRhtzN75AceXBQKXTkaojgLapfemY5p9C4d3vH2Ph1leJj0jjkfMXYrdEM7LTzcxYdletGBZuncYHa58CjibyGlJcRCr3DvsAs8lGs7i2AHi8Llbs/Zivd87mUOk2fPhIqRjJGTF/5JLLu9EyI57EpEiKCis5b1Q7zGYTlZU1TH3uGwryHRgMkNY0ps6+Bg3Jpv/ATH9C6j8r2bmjMPDcv6Z+y6GD5YHH541sxznD29CpSxpmsxG328vZQ/xJvD27i/jX1KWYzEZuu3MgqU2iT/h4N23I5c3XVpPWNIY77xmM0WggMTE4E7CLBI3FAhMnhjqKoFFSqp5NmzaNq666CoARI0ZQWlrKV199xdlnnx1oM2HCBD766COqqqq44IIL+M9//vOzfTqdTpzOoyW2R+agcrlcuFyu+j+IIDgS96kaf0MzmUwYjUbw+ShbMp3ksY8Qf+4kTBGxVO9eRfXOEysFL3j/ISK7nIe95RmAf1L06u3fBJ63Z/UBIKrzMNq+WlNrW0tSC8zx6bhLDgbWlX3zOj6jRb+3n6FzWxqzcDy/vV5PqEMIOYPVjLVbeuCxe2chru8PAGDp0jTQJmXe7+tsa+mWTs2yfRgsJmL/NhLLGekY4yMwmI5+mDam+j8wWrr6+/I53VTOWHV0f8d8MP0pls7+ZJZnTxHuzXkAOBfuwFtZgzHSirlTGhyTlHLvKjw6bNDr++UXoZHy+cLn2I9MZt6v1aW0TT2TCGssabGtGdP1LlKiW/La8rvJSOyC8YdEzHV9n+W6vs/W6qNVcvc6/a7c+zEAh8q2c6BkKy0TO9Msrj0APnxc2v0B2qX2I8aehMl49KNLfESTOn0t3f0ulS7/dbLP5wWgfdoAru/7PCnRGVjN9kBbk9FCjD3xZyuaMpOOVvms2PMRACVVuWzPX0mX9CFkJHats83Cra8Glo/E0JAsJhtZyT3wej2UVRewr3gD8za9yJ7CNZyZOZaJZ02hSUwWdktUre1i4+wUFVbSsmU8AOvX5lKQ7/ghbmolmI7oPzATgJlvfF8rIQXQpm0KE67qTlJSFJZjKh9NJiNR0Vaqq9zE/3D3vo3rc/F6fXhrPGzZdPikklLff+d/b8s7XHH0NbCYcLlcgetnn8+H2+0+5nn//GJutzus/k/JbxeO1yVy1In+XpSUqkdbt25lxYoVfPCBf4y72Wxm/PjxTJs2rVZS6tlnn+Whhx5i27Zt3Hfffdx555289NJLP9nvE088wSOPPFJn/WeffUZkZGS9H0cwLViwINQhhKX+/fuTnJxMdXU13spSypa+RfyQmwAo+fzFE+7HaIvCFJ0ceGxJrVtmDuAq2o+7eH/dJ0y13yI8pXnsLKikYO7cE47hdKVzWxqzcDq/m20voHmogwgxz4FS8oe/jLVfJglTx2Lt2ZzYR4dTMunDQBtfjRvXD8mgY3l/mMsp4V/jMGck4HN5cG8vwOd0Y+mQisFqxmA8TrVHA3+w8xbqrlsARUWFEPXL7YJlzf75rNk/HwMGWiZ24Zozn6Z5fAfOaD6c15bXbruveCNuj7PWuiLHgZPa3/V9n6Nj2kC8Pi+HSrfjdDtoGteGCEvMcauQjp3bCqBPxkVc2v0BAEoqD3OgZDPRtkRSYvx32DMaTHX6+K3KqvPrvc+fU1CRw/1zBtRZn53ci4ln/QOjwUiFs4j9eflYvEk0SfNXQBmPM4rjlzir3djsZgYNyWLbtnzcLn/SrXuPZowe0xGAstJqDh0qIyrKSlJy1K/e10+pqvInm7zHJKuLiorYtPlbevfuTXp6OuXl5SxatAiAmJgYhg71D/Vct24dOTk59RaLhI9wui6pDwaPh9Tv/TevyuveHZ+p/t+rgqGy8sT+lispVY+mTZuG2+0OTGwO/m+4bDYbU6dOJS7OP/lnWloaaWlptG/fnsTERAYOHMhf/vIXmjZtetx+jySujigrK6NFixYMHz6c2NjYhj2oBuJyuViwYAHDhg0LfHshR5l+eOOx2/3f6JV8/iLxQ27CXZZH+fJZJ9xPk2v/H5bE5jgPbsaS0oqY3pdQ0e/qwLC86t0riWx/Nu7Cvex/ejg+l//DiTmhGbbMnrgL9/2oRx9t2rShz6hRv/0gGymd29KYheP5Xbl3KVULl/9yw8bOBzXf7KHy7e+Jmtgb+9A2mDun4d5wCOgORiNljy4IVClhNWEbnE3Nsr0Y4uyYMxIAqJj6DY5XlmNKjyX50xtq7cK17hBc0R2DzUzkNb2ofM1fLWXKSsSTUwIuL75q/7eihoja54drQy62QVmYMhMxd0gNDN8zRvrnynBvzP3R8aiaASAxse4cTKEypsvdfJczl/0lm/DhY2/ROg6X7aZ5fAeqXP6qmn1F6/H6vBgNRpbu+i9fbJse2L51Su9Au2P1yhjN1ztnkRbbmmbx7QA4ULoFgKwkf2XV1zvf5q2VfybSGsdDIxcQYak7tAz8lVXHyvqhMqvKVc79cwbg9tZwRa/HGRxTe97MGvfRecuspghqPP7HewrXBdb3yRwTGL7XJsU/J9PeonWEq1bJ3QNVa4/MPQ9bQU/6Nb2NUaM71Gq3b18Jrdsk06VrGl9+sYPCAv+HyLSmMeQeqv37mjVzDeOv6EZWdhJXXduT119dhdfro2VmPADV1S6eeOwLPB4vYy/pTN/koxlVp9NNcXEVCQkRdOjYhCVf7cZkMtC+Y92Kt5OVlJhIj56j/KMM8CeiRv1wrWo8JqnetWtXunTp8pv3J+EjHK9L6oXDgWXcOABcxcUQFUbfTpyEIyO8fomSUvXE7Xbz+uuvM2XKFIYPH17ruYsuuoi3336bP/zhD3W283r93zAcOzzvx2w2Gzabrc56i8Vyyv/nawzH0KB++Gap5sBGdtySDB43PnfNL2zkF9P3SmLOvAyvy8mhly4nqutIUi57ktSrnqdqy5e4i3IofP9hIu5ZQESb/mQ9fwBX/m5MMSmY49Op2rYYx/cf1+nXaDTpd3YCdG5LYxZO57fReGp+e9hQHNNXEnlFdwxWM9E3nkXJXXOIvKYXlvapJL1zNe5dhRjMJkzpsRhsZvLP/Teeg2V4DpVhahpL9KT+2M/vgKlJND6Pl2PrG6rnbcZ1dQ8sndKIvXcIkVd2B7cXU4t48vpNxedy4t5dBPiHDSZ9OBFflYuiibOpnPk9EZd0xZQaTdLMK3HvK8ac6Z9I2bUtn6pPNx/naOR4c5KGSv/s8YzqPJny6kKKKg8SY0siMcr/ReyKvf6hbQWOHL7e+TaDWl/J+J4PM7Td9TjdDpIimxFhjWXGsrs4ULKlVr+XnHE/57S7gaSo5piMFpzuShZtmwHA/pIttE7pxYCsy2md0pu4iCYnlbA8MpdUhCWGxy9Ygttbc9yEVm75zsDyw+d/TmlVHu9+/1e25S1lU+4SOqYN5JLuf2FA9hUkRKZht0RT7apg3safHukQageOmUfroVHzqax0EmWtm+ScP3crmbckEBll5a57B5Of5yAm1sae3cW8/uqqWm0P7i/l9enfcf3v+9CxUxMuvbwbs2euCQz1s9st/OmBobg9Huz2un8jvlq0k4su7kxW6yTufWAoZrMRm/W3v4cbjIZaf5MMBsNx/0aZzfro21iF03VJvTjmWCwWS63Hp5IT/Z3o7nv15JNPPqG4uJgbbriBzp071/oZN24c06ZNY+7cuUyfPp0NGzawZ88ePv30U/7whz/Qv39/MjMzQ30IEua8juLABOW/xJzYgtSrXwCgaM5fqdm/geJ5U6jasRRTZBxpN84Ag4GqbUvIeeJsHGvngc+HNb0jeFxUfPc+xfP+0YBHIyIiDcGb76Dq400A2Ia2xpwRT9G1s3C88R3e3HLMmYkYYm24NuRS/txiPD8Mkyu5/SNq1h0CjxeDyUDJPZ/iK/7RXe9cXoomzsYxfSXunBJMTWIwxkdQs3Qvvhr/3F5V76+nev5WvGXVWNqmYO2WjsFkwFtUSeGEN6n6aCPecqd/QvbCSipnraHo2llQo7nBwt1H66awZv98qt0O0mKzibEnkVu2gznrn+XjdUcnL5+58n7eWf0I+0s2Ex+RSlJkMwoc+1mw5RW25dWdE/OFrybi8jgxGkwcKNnCi4tvoKTqMACvLbuLLYe/xeV1YjVFBPo9Ud/snM2CLa9QXl2I3RLFtrxlzFlf9/rmQMkWPtnwPKVVeSRFNScruQeRVv9ohJcW38DcjVMprMihSUwmXq+HNfvn89SCizl8TDIr3GzO/Zr31vyN4spcLCY7BaWH+eDd9XXa7d1TzEsvfMumDbk4nR5SUqOpcXrYs6vouP3u2FbAf2etxev10bN3cy4c24mVy/exeNFOKiqc2Owmdu0o5LN5W+ts++2SPXz+2TYcjhrsdjMb1+eyYrl/OJ1L7wEipy2DT7O91YsLLrgAr9fLp59+Wue5FStWcOaZZ/Lcc88xe/ZsNm3ahNPppEWLFlx88cX86U9/Ij4+/oT3VVZWRlxcHKWlpaf08L25c+cyatSoxpXVrmcFHzxC0UePhjqMWhLHPEjy2IdCHUbY0rktjVk4nt/lU7/B8dK3oQ5DpEF8fddBFkS+H+ow6l3fVpcE7nZ309sZIY7m9JBSMZKaTaNDGoPdbsZkNuKo8Ff9m81GJt8xgKbpsezdU8yLz3/zCz0c37nntWH4iHb1GaqcIsLxuqReOBwQ/cMNACoqTunheyeSt1ANYz2ZM2fOTz7Xp0+fwJ0ebrvttmCFJCIiIiIiEhYSEiOZdHt/cvaVUF3lolnzeOLi7Xg8Xj77X93KKhE5PSgpJSIiIiIiIg3K4ahh964i0pvFEhFhobrKzeZNh1m0cOdPDhcUkcZPSSkREREREQmapbvfZenud0MdhgRZWWk1//mX7pYqIrUpKSUiIiIiIiIiEg6sVpg69ehyI6eklIiIiIiIiIhIOLBY4JZbQh1F0BhDHYCIiIiIiIiIiJx+VCklIiIiIiIiIhIOPB5YssS/PHAgmEyhjaeBKSklIiIiIiIiIhIOqqthyBD/ckUFREWFNp4GpuF7IiIiIiIiIiISdEpKiYiIiIiIiIhI0CkpJSIiIiIiIiIiQaeklIiIiIiIiIiIBJ0mOhcJY6boRCyp2aEOoxZTdGKoQxARCTDG2TG1iA91GHKK8+Gj0lFJZFQkBgyhDicgylZFSnRGqMOQU5zP58PqiCQ6KQKDIXzO7/oSGWkNdQgi8hsoKSUSxhKGTSZh2ORQhyEiErairu5J1NU9Qx2GnOJcLhdL585l1KhRWCyWUIcTMBIYyYOhDkNOcS6Xi7lz5zLq2kFhdX6LiICSUiIiIiIiIiIi4cFigaefPrrcyCkpJSIiIiIiIiISDqxW+L//C3UUQaOJzkVEREREREREJOhUKSUiIiIiIiIiEg48Hli92r/coweYTKGNp4EpKQV4PB4++OADNm/eDECHDh246KKLMJv18oiIiIiIiIhIkFRXQ58+/uWKCoiKCm08Dey0z7ps3LiRCy+8kNzcXNq1awfAU089RUpKCnPmzKFz584hjlBEREREREREpPE57eeU+t3vfkenTp3Yv38/q1evZvXq1eTk5NC1a1duvPHGUIcnIiIiIiIiItIonfaVUmvWrGHVqlUkJCQE1iUkJPDXv/6V3r17hzAyEREREREREZHG67SvlGrbti2HDx+usz4vL4/WrVuHICIRERERERERkcbvtExKlZWVBX6eeOIJbr31Vt59913279/P/v37effdd7n99tt56qmnQh2qiIiIiIiIiEijdFoO34uPj8dgMAQe+3w+LrvsssA6n88HwAUXXIDH4wlJjCIiIiIiIiIijdlpmZRatGhRqEMQEREREREREanNYoGHHjq63MidlkmpwYMHhzoEEREREREREZHarFZ4+OFQRxE0p2VS6sdKSkqYNm0amzdvBqBTp05cf/31xMXFhTgyEREREREREZHG6bSc6PxYq1atIjs7m2effZaioiKKior4xz/+QXZ2NqtXrw51eCIiIiIiIiJyuvB6YeNG/4/XG+poGtxpXyl1xx13cOGFF/LKK69gNvtfDrfbze9+9ztuv/12Fi9eHOIIRUREREREROS0UFUFnTv7lysqICoqtPE0sNM+KbVq1apaCSkAs9nMPffcQ69evUIYmYiIiIiIiIhI43XaD9+LjY1l3759ddbn5OQQExMTgohERERERERERBq/0z4pNX78eG644QZmz55NTk4OOTk5zJo1i9/97ndMmDAh1OGJiIiIiIiIiDRKp/3wvWeeeQaDwcA111yD2+3G5/NhtVr54x//yJNPPhnq8EREREREREREGqXTPilltVp5/vnneeKJJ9i5cycA2dnZREZGhjgyEREREREREZHG67RNSl188cW/2MZsNpOWlsawYcO44IILghCViIiIiIiIiMjp4bRNSsXFxf1iG6/Xy/bt2/nPf/7D3XffzaOPPhqEyERERERERETktGSxwN13H11u5E7bpNT06dNPuO0nn3zCzTffrKSUiIiIiIiIiDQcqxX+/vdQRxE0p/3d907EgAED6NWrV6jDEBERERERERFpNE7bSqmTER8fz/vvvx/qMERERERERESkMfN6Yd8+/3LLlmBs3LVESkqJiIiIiIiIiISDqipo1cq/XFEBUVGhjaeBNe6Um4iIiIiIiIiIhCUlpUREREREREREJOg0fE/kB8UL/knJgn+GOoxa4odNJmHY5FCHIY2A443vqHxzdajDkFOcDx/dHJUUvzQdA4ZQhwPAqqsP8U3c56EOQ05xPp8PW35vNj8VjcEQHud2feo/sBUDBrUKdRgiIiJ1KCkl8gNPRRGuvJ2hDqMWT0VRqEOQRsJbWo0npyTUYUgjYAe8RaWhDiPA4Swmv2JvqMOQRiDF14mKwqpQh9EgKitrQh2CiIjIcWn4noiIiIiIiIiIBJ2SUiIiIiIiIiIiEnQaviciIiIiIiIiEg7MZrj55qPLjVzjP0IRERERERERkVOBzQYvvhjqKIJGw/dERERERERERCToVCklIiIiIiIiIhIOfD4oKPAvJyeDwRDaeBqYklIiIiIiIiIiIuGgshJSU/3LFRUQFRXaeBqYhu+JiIiIiIiIiEjQKSklcpLMyRm0neGh7QwPEe0HhzocERERCbGs7CSefnY0Tz87moSEiKDs86Zb+vL0s6O5bEK3oOxPRESkIWj4nsgPksc+REzPi9j7YI/AOmNUIlnP7sNo9V9gFn36FMWfvUDVzmUAeKvKQhKriJyYxBnjsfZpGXjsc3vxllTh+m4/5c98hedAadBjivvrSCLGdqZmxT6KJs4O+v6lYZmMFoa3v5E+mWNJimqG1+ehvLqQAyVb+GTDc+wv2RzqEH+1vq0uYeJZUwC46e2MEEdzYu4cOot2TfoGHnu9Hkqr88kp3sC8TS+xq+C7EEb32xzOLcdsNlJYUBnqUERERH41JaVEjmFr2Y2ItgOp2rYEgLjBNwQSUkd4SnPJeax/KMITkV/JV+PGtTkPY5wdc2YipvPaYcpKpHDMjFCHVj8sRnB5Qx2FAOPO+DPntLsegMNlu3B5nSRFNad7ixGs2PtR2CWlTEYLHq8rpDEYDWa8PneD7sPlcZJTvBGzyUazuLZ0bXYunZoO5ukF49hTtLZB991QPnxvQ6hDEBER+c2UlGoAS5cuZcCAAYwYMYJPP/00sH7t2rU8+eSTfP311xQUFJCZmckf/vAHbrvtthBGK0f4vB4MRhPxwyb5k1IGI/FD/4jPXYPBbA20MydnkPXMLgBynhyKc89qMh5fgyU5k9IlMzg87QYMtigyH1+LJaUVZd+8Qe4rEwGwZ/UhacyD2Nv0xWCxU3NgE0WfPEnFqvcC/bed4QEgf/a9xJx5WfBeAJFGzJvvoGjCWwDEPTGKiDGdsLRJwRBnx1dajSHaSvTkAdjPaY0xJRpvYSXV/9tK+QtLoNr/YdnaN4Pom/thykzAGGvH53Tj3ppPxcvLqFmyO7AvQ6SF6Fv6YzunDaamMfgcNbjW51J864ekfHI9pmZx/v76tCRt0/8BUHTtLGpW5mBsGkPMrQOx9s/EGGfHW1iJc9EOyl/4Gl9ptT/+YyqtnIt3E3lVD4yp0Rzu/EwwX1L5Cb1ajgbgk/XPMWfDs4H12cm9KK8uCDw2YGBI24kMyJ5AakwGNR4nW3K/5r01T1DoyAFqVyZNWTieS3s8SNPYbA6X72bWqofYnr8cgObxHbm0+wM0jWtDpDUOr8/DodLtfLFtOsv3fBDY578n7AXgvTV/IyupBx3SBrI6Zy6vLb+biWdNISu5J3H2FMxGK6XVeazdv4CP1j1DtbuCa898hn5Zl9bpa876Z/lkw3NYTDZGdbqVXi1HkxiZjtNdyfb85Xy0bgoHS7fWOZ4XvryGS7o/QJOYLJ5bdCXb8pbV7y/iR0qr8nhqwVgAOqcPZfLg6ZiMFvpkjqF7ixF0bXYuCfbmWM12HBU1bNuWz7xPtlBe5gz00bxFHOcMa0NmViI2m4nS0mqWfr2XxV/uOu4+R1/YgUFDsqmp8TBj2kp2bCvgsiu6kZmZSEyMDZPZSHlZNRs3HGb+3K04nUcTc8NHtqVvv0yMJgOrVx3AWe1i6LA2ANxzxyeAf/heduskVq3I4Z2315KQEMF9D54DwOyZa+jarSnZbZKpKHdy6KAqu0VEJDxpTqkGMG3aNCZPnszixYs5ePBgYP13331Hamoqb775Jhs3buT+++/nvvvuY+rUqSGMVo5wl+RSk7eT6O5jMCc0I7r7hViSMyhf+d7PbuetLif35Yn4vB7iBk4kstO5pFz2JJaUVrjyd5P3xmQA7K370eLPXxHVbSS+mircBXuwZ/YgfdI7xPS7uk6/SRc/iiWlVYMcq4iAt6wan6MGLEYSX7ucqKt7YkyMxL2zEGO8naiJvUh46eJAe3PrZCxdm+JzuHBvLwCDAWvP5iRMHYu5XYq/0ZG+ruuNuWU83rwKvKXVWPtnYrCacG3Ow1vkH2rjrXBSs/YgNWsP4q1wYkyMJGnmlUSM6YQxxoZ7bzHGpEgiJ3Qn8bXLwWqqFb/ljHSi7xiIz+HEV1oVtNdNfp7B4L+06th0EF3ShxJjTwZgZ8Eq8ir2BNpd3usxxvd8mGbx7cgr34vP56Fny/O5d9j7xNiS6vQ7afB0zEYrPp+P5vEdmDR4OnER/jvzJEU1p23qWbg8Tg6VbsftqSEzqRvX932OzulD6/R1YZe7aN+kH4WOnECVVLdmw4iyxpFfsZfiykMkRTVnaLvruObMpwHIr9hHfvneQB+7Clazq2A1xZW5ANw8aBqjOk0iObol+RX7MBnNnNH8PO4d9j5NYrLrxPDHga9gNUVQXHno17zM9apT08HERzSh1FFMQYGD6BgbvXq34NrrewfaZGQmcPOt/ejUJQ2bzURBvgO7zUJmVuJx+xw+oq0/IeV0M/2VFezY5k9IduqcRkSEhcJCB6UlVSQkRjJgUCsuvbxrYNt+AzM5d3hboqKtOJ1uup7RlP6DTu56YNxlXWmSFoPH4yUxKZKOnZv8ildGRESk4alSqp5VVFQwe/ZsVq1aRW5uLjNmzODPf/4zANdff32ttllZWSxdupT333+fSZMmhSJcqcVHycKXSJ0whbihfyAi+ywASj6fSmzfCT+7ZdW2JRTPe4bE8+8l7cbXMcWk4PN6OPTytXirywFIHvcoBrMVx4YFHPjH+eD1kDJhCgnn3U7yuMco//aNWn268ndRsfpjkkbf2zCHK3IaMaZEkfj2lYHhe96SKkr/Mh/cXuxjOmHp0ARfjZuCsTPw7C3B3C6F5A8mYjsrA+tZLalZto/qhdup+nADvnJ/5YQh1kbK5zdhjLZhH96Wiq352Ed2wNIpDYCyZ76k8tWVAJhbJ+GrdlFy64eBSif3psO15pSKntQfU5MYfB4vhVe8hXtzHrZzWpPwz7FY2qYQcX4Hqj44OlzHYDVTdNO7/iotoyGIr6b8nK+2v8EFXe4gK7kHkwZPByC3bAfL93zEZ5v/jdvrJCmqBYNaXwnA9KV3sGzP+9jMkTw8aiGJUekMaTuRj9dPqdXvf1c/xpKdM2ka24a/jJyH3RLF0LbX8cHap9hd+D33fNQnUIllNtp4cNR8msS0onfLC9hw8ItafRVU5PD0grFUusoCSbQpC8fXGlo4psvdjOo8mTOaD8dstDF34wsUVx4MVDodqToCaJval45pAwF49/vHWLj1VeIj0njk/IXYLdGM7HQzM5bdVSuGhVun8cHap4CjibyGFBeRyr3DPggM3wPweF2s2PsxX++czaHSbSRXjKBm02j6nNmCSy7vRsuMeBKTIikqrOS8Ue0wm01UVtYw9blvKMh3YDBAWtOYOvsaNCSb/gMz/Qmp/6xk547CwHP/mvothw6WBx6fN7Id5wxvQ6cuaZjNRtxuL2cP8Sfx9uwu4l9Tl2IyG7ntzoGkNok+4ePdtCGXN19bTVrTGO68ZzAGg94jREROGWYzXHvt0eVGrvEfYZC98847tG/fnnbt2nHVVVdx++23c9999/3kxUBpaSmJicf/lu0Ip9OJ03m0fLyszF+C7XK5cLlCOw/Er3Uk7nCI32QyYTQaweejbMl0ksc+Qvy5kzBFxFK9exXVO09sSEHB+w8R2eU87C3PAPyToldv/ybwvD2rDwBRnYfR9tWaWttaklpgjk/HXXK0sq7sm9fxGS1h8RrJiQunc/tYXq8n1CGElMFqxtotPfDYvbMQ1/cHALB0aRpokzLv93W2tXRLp2bZPgwWE7F/G4nljHSM8REYTEc/SBtT/R8WLV39ffmcbipnrDq6v2M+lP4US2d/Msuzpwj35jwAnAt34K2swRhpxdwpDY5JSrl3FR4dNuj1/fKL0Ej5fOF17EcmM+/X6lLapp5JhDWWtNjWjOl6FynRLXlt+d1kJHbB+EMi5rq+z3Jd32dr9dEquXudflfu/RiAQ2XbOVCylZaJnWkW1x4AHz4u7f4A7VL7EWNPwmQ8enkXH1G3Qmbp7nepdPmvJXw+/1xk7dMGcH3f50mJzsBqtgfamowWYuyJP1vRlJl0tMpnxZ6PACipymV7/kq6pA8hI7FrnW0Wbn01sHwkhoZkMdnISu6B1+uhrLqAfcUbmLfpRfYUruHMzLFMPGsKaTFtsFnstbaLjbNTVFhJy5bxAKxfm0tBvuOHuKmVYDqi/8BMAGa+8X2thBRAm7YpTLiqO0lJUViOqX40mYxERVuprnIT/8Pd+zauz8Xr9eGt8bBl0+GTSkp9/53//S3vcEVgncfjwev1Bq57fD4fbvfRIYMWiwUAt9sddv+v5LcJ12sTkd+q0Z7bRiO88srRx6fo8Z3o70VJqXo2bdo0rrrqKgBGjBhBaWkpX331FWeffXadtt9++y2zZ8+uNe/U8TzxxBM88sgjddZ/9tlnREZG1kvcobJgwYJQh0D//v1JTk6muroab2UpZUvfIn7ITQCUfP7iCfdjtEVhik4OPLak1h2uAOAq2o+7eH/dJ0y1/zt6SvPYWVBJwdy5JxyDhI9wOLeP1Wx7Ac1DHUQIeQ6Ukj/8Zaz9MkmYOhZrz+bEPjqckkkfBtocmQz9x7w/zOWU8K9xmDMS8Lk8uLcX4HO6sXRIxWA1YzAep9KjgT/UeQt1xy2AoqJCiAp1FLWt2T+fNfvnY8BAy8QuXHPm0zSP78AZzYfz2vLabfcVb8TtcdZaV+Q4cFL7u77vc3RMG4jX5+VQ6XacbgdN49oQYYk5bhXSsXNbAfTJuIhLuz8AQEnlYQ6UbCbalkhKjP8Oe0aDqU4fv1VZdX699/lzCipyuH/OgDrrs5N7MfGsf2A0GHFUV3D4YAk2m4kmaf4KKOOvqDByVrux2c0MGpLFtm35uH+4CUH3Hs0YPaYjAGWl1Rw6VEZUlJWk5Khfva+fUlXlTzZ5j0lYb9++na1bt9K7d2/S09MpLy9n0aJFAMTExDB0qH+o57p168jJyam3WCR8hNu1iUh90bkdniorT+xaVUmperR161ZWrFjBBx/4JxU1m82MHz+eadOm1UlKbdiwgTFjxvDQQw8xfPjwn+33vvvu48477ww8Lisro0WLFgwfPpzY2Nh6P45gcLlcLFiwgGHDhgW+mQsVk8l/sW23+78dLfn8ReKH3IS7LI/y5bNOuJ8m1/4/LInNcR7cjCWlFTG9L6Gi39WBYXnVu1cS2f5s3IV72f/0cHwu/wddc0IzbJk9cRfu+1GPPtq0aUOfUaN++0FK0ITTuX2syr1LqVq4/JcbNmY+qPlmD5Vvf0/UxN7Yh7bB3DkN94ZDQHcwGil7dEGgSgmrCdvgbGqW7cUQZ8eckQBAxdRvcLyyHFN6LMmf3lBrF651h+CK7hhsZiKv6UXla/5qKVNWIp6cEnB58VX7vzUyRNQ+P1wbcrENysKUmYi5Q2pg+J4x0n+jBffG3B8djyoZABIT686/FEpjutzNdzlz2V+yCR8+9hat43DZbprHd6DK5a+q2Ve0Hq/Pi9FgZOmu//LFtumB7Vun9A60O1avjNF8vXMWabGtaRbfDoADpVsAyEryV1Z9vfNt3lr5ZyKtcTw0cgERlrpDy8BfWXWsrB8qs6pc5dw/ZwBubw1X9HqcwTG15zuscR+du8xqiqDG43+8p3BdYH2fzDGB4XttUvxzMu0tWke4apXcPVC19s93H6Xou0GcfU42o0Z3qNVu374SWrdJpkvXNL78YgeFBf4L7bSmMeQeqv37mjVzDeOv6EZWdhJXXduT119dhdfro2VmPADV1S6eeOwLPB4vYy/pTN/ko1lVp9NNcXEVCQkRdOjYhCVf7cZkMtC+42+fE6pNmzZkZ2f7q8PxJ6JG/XCNYTwmsd61a1e6dOnym/cn4SNcr01EfqtGe277fHAkoRMZCafoEOwjI7x+iZJS9WjatGm43W7S048OEfH5fNhsNqZOnUpcnP+OS5s2beKcc87hxhtv5IEHHvjFfm02Gzabrc56i8Vyyv/nC6tj+OE/e82Bjey4JRk8bnzuml/YyC+m75XEnHkZXpeTQy9dTlTXkaRc9iSpVz1P1ZYvcRflUPj+w0Tcs4CINv3Jev4ArvzdmGJSMMenU7VtMY7vP67Tr9FoCp/XR05KWJ3b+M8l8XNMX0nkFd0xWM1E33gWJXfNIfKaXljap5L0ztW4dxViMJswpcdisJnJP/ffeA6W4TlUhqlpLNGT+mM/vwOmJtH4PF6OvUyonrcZ19U9sHRKI/beIURe2R3cXkwt4snrNxWfy4l7dxHgHzaY9OFEfFUuiibOpnLm90Rc0hVTajRJM6/Eva8Yc6Z/eLdrWz5Vn24+ztFIuM2V0z97PKM6T6a8upCiyoPE2JJIjPJfF6zY6x/aVuDI4eudbzOo9ZWM7/kwQ9tdj9PtICmyGRHWWGYsu4sDJVtq9XvJGfdzTrsbSIpqjslowemuZNG2GQDsL9lC65ReDMi6nNYpvYmLaHJSScsjc0lFWGJ4/IIluL01x01o5ZbvDCw/fP7nlFbl8e73f2Vb3lI25S6hY9pALun+FwZkX0FCZBp2SzTVrgrmbXzppF7DYDpwzDxaky95kIphPqKj615zzZ+7lcxbEoiMsnLXvYPJz3MQE2tjz+5iXn91Va22B/eX8vr077j+933o2KkJl17ejdkz1wSG+tntFv70wFDcHg92e92/E18t2slFF3cmq3US9z4wFLPZiM3629/DTSZT4Is48P/fOd7fKfNpMH/J6Srcrk1E6kujO7cdDkjwfxlKRQVEhVlJ+Ak60d+J7r5XT9xuN6+//jpTpkxhzZo1gZ+1a9eSnp7O22+/DcDGjRsZMmQI1157LX/9619DHLX8FK+jODBB+S8xJ7Yg9eoXACia81dq9m+geN4UqnYsxRQZR9qNM8BgoGrbEnKeOBvH2nng82FN7wgeFxXfvU/xvH804NGIyLG8+Q6qPt4EgG1oa8wZ8RRdOwvHG9/hzS3HnJmIIdaGa0Mu5c8txvPDMLmS2z+iZt0h8HgxmAyU3PMpvuIf3fXO5aVo4mwc01fizinB1CQGY3wENUv34qvxz+tV9f56qudvxVtWjaVtCtZu6RhMBrxFlRROeJOqjzbiLXf6J2QvrKRy1hqKrp0FNaf3vGCnio/WTWHN/vlUux2kxWYTY08it2wHc9Y/y8frjk5ePnPl/byz+hH2l2wmPiKVpMhmFDj2s2DLK2zLqzuX4QtfTcTlcWI0mDhQsoUXF99ASdVhAF5bdhdbDn+Ly+vEaooI9Huivtk5mwVbXqG8uhC7JYptecuYs77u36UDJVv4ZMPzlFblkRTVnKzkHkRa/RXbLy2+gbkbp1JYkUOTmEy8Xg9r9s/nqQUXc/iYZFa42Zz7Ne+t+RvFlblYTBbyDzv44N31ddrt3VPMSy98y6YNuTidHlJSo6lxetizq+i4/e7YVsB/Z63F6/XRs3dzLhzbiZXL97F40U4qKpzY7CZ27Sjks3lb62z77ZI9fP7ZNhyOGux2MxvX57JiuX84nUvvAyIi0sgYfJrJsF58+OGHjB8/nry8vEBF1BH33nsvX3zxBdOnT2fo0KGcd955/P3vfw88bzKZSElJOeF9lZWVERcXR2lp6Sk9fG/u3LmMGjUqbLLaBR88QtFHj4Y6jFoSxzxI8tiHQh2GnIRwPLcByqd+g+Olb0Mdhki9+/qugyyIfD/UYTSIvq0uCdzt7qa3M0IcTeOXUjGSmk2jQx0GdrsZk9mIo8JfrW02G5l8xwCapseyd08xLz7/zS/0UNe557Vh+Ih29R2qnCLC9dpE5LdqtOe2wwHRP9zc4hSulDrRvIXqc+vJtGnTOPfcc+skpADGjRvH008/zYMPPkh+fj5vvvkmb775ZuD5jIwM9uzZE8RoRURERCQcJSRGMun2/uTsK6G6ykWz5vHExdvxeLx89r+6lVUiIiKnMiWl6smcOXN+8rk+ffro1roiIiIi8oscjhp27yoivVksEREWqqvcbN50mEULd/7kcEEREZFTlZJSIiIiImFo6e53Wbr73VCHIUFWVlrNf/51mt8tVUREThua6FxERERERERERIJOlVIiIiIiIiIiIuHAZIJLLjm63MgpKSUiIiIiIiIiEg7sdvjvf0MdRdBo+J6IiIiIiIiIiASdklIiIiIiIiIiIhJ0SkqJiIiIiIiIiIQDhwMMBv+PwxHqaBqcklIiIiIiIiIiIhJ0SkqJiIiIiIiIiEjQKSklIiIiIiIiIiJBZw51ACLhwhSdiCU1O9Rh1GKKTgx1CNJIGOPsmFrEhzoMOcX58FHpqCQyKhIDhlCHA0CUrYqU6IxQhyGnOJ/Ph9URSXRSBAZDeJzb9Sky0hrqEERERI5LSSmRHyQMm0zCsMmhDkOkQURd3ZOoq3uGOgw5xblcLpbOncuoUaOwWCyhDgeAkcBIHgx1GHKKc7lczJ07l1HXDgqbc1tEROR0oOF7IiIiIiIiIiISdKqUEhEREREREREJByYTjBp1dLmRU1JKRERERERERCQc2O3w6aehjiJoNHxPRERERERERESCTkkpEREREREREREJOiWlRERERERERETCgcMBUVH+H4cj1NE0OM0pJSIiIiIiIiISLiorQx1B0KhSSkREREREREREgk5JKRERERERERERCTolpUREREREREREJOiUlBIRERERERERkaBTUkpERERERERERIJOd98TEREREREREQkHRiMMHnx0uZFTUkpEREREREREJBxERMCXX4Y6iqBp/Gk3EREREREREREJO0pKiYiIiIiIiIhI0CkpJSIiIiIiIiISDhwOSEnx/zgcoY6mwWlOKRERERERERGRcFFQEOoIgkaVUiIiIiIiIiIiEnRKSomIiIiIiIiISNApKSUiIiIiIiIiIkGnpJSIiIiIiIiIiASdklIiIiIiIiIiIhJ0uvueiIiIiIiIiEg4MBqhV6+jy42cklIiIiIiIiIiIuEgIgJWrgx1FEHT+NNuIiIiIiIiIiISdpSUEhERERERERGRoFNSSkREREREREQkHFRWQmam/6eyMtTRNDjNKSUiIiIiIiIiEg58Pti79+hyI6eklIRMws5Pyfnz3RgMhlCHAkC09Y+wMjrUYcgpzoePbo5Kil+ajoHwOLcBVl19iG/iPg91GHKK8/l82PJ7s/mp6LB5765P/Qe2YsCgVqEOQ0REROS0oaSUhIypphx3/q5QhxHgja7Cl+MOdRjSCNgBb1FpqMOoxeEsJr9ib6jDkEYgxdeJisKqUIfRICora0IdgoiIiMhpRXNKiYiIiIiIiIhI0CkpJSIiIiIiIiIiQaeklIiIiIiIiIiIBJ3mlBIRERERERERCQcGA3TseHS5kVNSSkREREREREQkHERGwsaNoY4iaDR8T0REREREREREgk5JKRERERERERERCTolpUREREREREREwkFlJXTq5P+prAx1NA1Oc0pJ2DMnZ5D1zC4Acp4cStWWr0IckYiInKys7CT+MKkvAE88upDi4qoG3+dNt/Qlu3USq1bk8M7baxt8fyIiIiK/mc8HmzYdXW7klJSSkLBYLPT/09s49/2JvQ/2CKw3RiWS9ew+jNYIAIo+fYriz16gaucyALxVZSGJt7FLnDEea5+Wgcc+txdvSRWu7/ZT/sxXeA6UBj2muL+OJGJsZ2pW7KNo4uyg718ajsloYXj7G+mTOZakqGZ4fR7Kqws5ULKFTzY8x/6SzaEO8Vfr2+oSJp41BYCb3s4IcTQn5s6hs2jXpG/gsdfrobQ6n5ziDczb9BK7Cr4LYXS/zeHccsxmI4UFjf9bRhEREZFTkZJSElK2lt2IaDuQqm1LAIgbfEMgIXWEpzSXnMf6BzcwixFc3uDuMwz4aty4NudhjLNjzkzEdF47TFmJFI6ZEerQ6sdp+nsNN+PO+DPntLsegMNlu3B5nSRFNad7ixGs2PtR2CWlTEYLHq8rpDEYDWa8PneD7sPlcZJTvBGzyUazuLZ0bXYunZoO5ukF49hTdGpWGX343oZQhyAiIiIiP0NJqQawdOlSBgwYwIgRI/j0009rPXfrrbfyzTffsGHDBjp06MCaNWtCE2QY8HrcGE1m4odN8ielDEbih/4Rn7sGg9kaaPfj4XvOPavJeHwNluRMSpfM4PC0GzDYosh8fC2WlFaUffMGua9MBMCe1YekMQ9ib9MXg8VOzYFNFH3yJBWr3gv033aGB4DyD7/AMqY51v6ZOOdvo/T+ecF7McKEN99B0YS3AIh7YhQRYzphaZOCIc6Or7QaQ7SV6MkDsJ/TGmNKNN7CSqr/t5XyF5ZAtf8Ds7VvBtE398OUmYAx1o7P6ca9NZ+Kl5dRs2R3YF+GSAvRt/THdk4bTE1j8DlqcK3PpfjWD0n55HpMzeL8/fVpSdqm/wOg6NpZ1KzMwdg0hphbB2Ltn4kxzo63sBLnoh2Uv/A1vtJqf/zHVFo5F+8m8qoeGFOjOdz5mWC+pHIcvVqOBuCT9c8xZ8OzgfXZyb0ory4IPDZgYEjbiQzInkBqTAY1Hidbcr/mvTVPUOjIAWpXJk1ZOJ5LezxI09hsDpfvZtaqh9ievxyA5vEdubT7AzSNa0OkNQ6vz8Oh0u18sW06y/d8ENjnvyfsBeC9NX8jK6kHHdIGsjpnLq8tv5uJZ00hK7kncfYUzEYrpdV5rN2/gI/WPUO1u4Jrz3yGflmX1ulrzvpn+WTDc1hMNkZ1upVeLUeTGJmO013J9vzlfLRuCgdLt9Y5nhe+vIZLuj9Ak5gsnlt0JdvyltXvL+JHSqvyeGrBWAA6pw9l8uDpmIwW+mSOoXuLEXRtdi7xEU2wmaNxlLvYti2feZ9sobzMGeijeYs4zhnWhsysRGw2E6Wl1Sz9ei+Lv9x13H2OvrADg4ZkU1PjYca0lezYVsBlV3QjMzORmBgbJrOR8rJqNm44zPy5W3E6jybmho9sS99+mRhNBlavOoCz2sXQYW0AuOeOT4C6w/cSEiK478FzAJg9cw1duzUlu00yFeVOFi7Y3iCvq4iIiIj8NE103gCmTZvG5MmTWbx4MQcPHqzz/PXXX8/48eNDEFl4cRzeQ03eTqK7j8Gc0Izo7hdiSc6gfOV7P7udt7qc3Jcn4vN6iBs4kchO55Jy2ZNYUlrhyt9N3huTAbC37keLP39FVLeR+GqqcBfswZ7Zg/RJ7xDT7+o6/UafPwjrWRl49pfic3ka5JhPRd6yanyOGrAYSXztcqKu7okxMRL3zkKM8XaiJvYi4aWLA+3NrZOxdG2Kz+HCvb0ADAasPZuTMHUs5nYp/kZH+rquN+aW8XjzKvCWVmPtn4nBasK1OQ9vkX+4jbfCSc3ag9SsPYi3wokxMZKkmVcSMaYTxhgb7r3FGJMiiZzQncTXLgerqVb8ljPSib5jID6HE19pw89hI7/MYPD/6enYdBBd0ocSY08GYGfBKvIq9gTaXd7rMcb3fJhm8e3IK9+Lz+ehZ8vzuXfY+8TYkur0O2nwdMxGKz6fj+bxHZg0eDpxEakAJEU1p23qWbg8Tg6VbsftqSEzqRvX932OzulD6/R1YZe7aN+kH4WOnECVVLdmw4iyxpFfsZfiykMkRTVnaLvruObMpwHIr9hHfvneQB+7Clazq2A1xZW5ANw8aBqjOk0iObol+RX7MBnNnNH8PO4d9j5NYrLrxPDHga9gNUVQXHno17zM9apT08HERzShuPIQhaV5RMfY6NW7Bdde3zvQJiMzgZtv7UenLmnYbCYK8h3YbRYysxKP2+fwEW39CSmnm+mvrGDHNn9CslPnNCIiLBQWOigtqSIhMZIBg1px6eVdA9v2G5jJucPbEhVtxel00/WMpvQf1OqkjmncZV1pkhaDx+MlMSmScZd1JTLK+ssbioiIiEi9UaVUPauoqGD27NmsWrWK3NxcZsyYwZ///OfA8y+88AIA+fn5rFu3LlRhhgefj5KF/4/UCVOIG/oHIrLPAqDk86nE9p3ws5tWbVtC8bxnSDz/XtJufB1TTAo+r4dDL1+Lt7ocgORxj2IwW3FsWMCBf5wPXg8pE6aQcN7tJI97jPJv36jVp6ewhMKLZuIrc4LR0DDHHOaMKVEkvn1lYPiet6SK0r/MB7cX+5hOWDo0wVfjpmDsDDx7SzC3SyH5g4nYzsrAelZLapbto3rhdqo+3ICv3F89YYi1kfL5TRijbdiHt6Viaz72kR2wdEoDoOyZL6l8dSUA5tZJ+KpdlNz6YaDSyb3pcK05paIn9cfUJAafx0vhFW/h3pyH7ZzWJPxzLJa2KUSc34GqD44O2TFYzRTd9K6/Sus0/b2Gm6+2v8EFXe4gK7kHkwZPByC3bAfL93zEZ5v/jdvrJCmqBYNaXwnA9KV3sGzP+9jMkTw8aiGJUekMaTuRj9dPqdXvf1c/xpKdM2ka24a/jJyH3RLF0LbX8cHap9hd+D33fNQnUIllNtp4cNR8msS0onfLC9hw8ItafRVU5PD0grFUusoCSbQpC8fXGlo4psvdjOo8mTOaD8dstDF34wsUVx4MVDodqToCaJval45pAwF49/vHWLj1VeIj0njk/IXYLdGM7HQzM5bdVSuGhVun8cHap4CjibyGFBeRyr3DPggM3wPweF2s2PsxX++czaHSbfjwkVIxkjNi/sgll3ejZUY8iUmRFBVWct6odpjNJiora5j63DcU5DswGCCtaUydfQ0akk3/gZn+hNR/VrJzR2HguX9N/ZZDB8sDj88b2Y5zhrehU5c0zGYjbreXs4f4k3h7dhfxr6lLMZmN3HbnQFKbRJ/w8W7akMubr60mrWkMd94zGKPRQGJixC9vKCIiIiL1RkmpevbOO+/Qvn172rVrx1VXXcXtt9/Offfdh8Hw6z8MO51OnM6jwyPKyvyTfbtcLlyu0M5z8muYTCaMRv8HrLIl00ke+wjx507CFBFL9e5VVO88sSEqBe8/RGSX87C3PAPwT4pevf2bwPP2rD4ARHUeRttXa2pta0lqgTk+HXfJ0Uq2qmXr/AkpAG/jv8vB8RisZqzd0gOP3TsLcX1/AABLl6aBNinzfl9nW0u3dGqW7cNgMRH7t5FYzkjHGB+BwXT0w7Qx1f+B0dLV35fP6aZyxqqj+zvmg+lPsXT2J7M8e4pwb84DwLlwB97KGoyRVsyd0uCYpJR7V+HRYYOn6e8VwBdGd+44Mpl5v1aX0jb1TCKssaTFtmZM17tIiW7Ja8vvJiOxC8YfEjHX9X2W6/o+W6uPVsnd6/S7cu/HABwq286Bkq20TOxMs7j2APjwcWn3B2iX2o8YexIm49E/f/ERTer0tXT3u1S6/O+1Pp9/HrL2aQO4vu/zpERnYDXbA21NRgsx9sSfrWjKTDpa5bNiz0cAlFTlsj1/JV3Sh5CR2LXONgu3vhpYPhJDQ7KYbGQl98Dr9VBWXcC+4g3M2/QiewrXcGbmWCaeNYUmMVnYLVG1touNs1NUWEnLlvEArF+bS0G+44e4qZVgOqL/wEwAZr7xfa2EFECbtilMuKo7SUlRWI6pfDSZjERFW6muchOf4E8ebVyfi9frw1vjYcumwyeVlPr+O/97W97hiqOvgcWEy+UK/J3y+Xy43e5jnrcA4Ha7w+r/lPx2R66nTsXrKpFfovNbGqtGe2673ZgzMn5YdMMpenwn+ntRUqqeTZs2jauuugqAESNGUFpayldffcXZZ5/9q/t84okneOSRR+qs/+yzz4iMjPzV/YZK//79SU72D9fxVpZStvQt4ofcBEDJ5y+ecD9GWxSm6OTAY0tq3eEvAK6i/biL99d9wlT79C87dBhT3VanFc+BUvKHv4y1XyYJU8di7dmc2EeHUzLpw0CbI5Oh/5j3h7mcEv41DnNGAj6XB/f2AnxON5YOqRisZgzG41R7NPAHO2+h7roFUFRUCFG/3C5Y1uyfz5r98zFgoGViF64582max3fgjObDeW157bb7ijfi9jhrrStyHDip/V3f9zk6pg3E6/NyqHQ7TreDpnFtiLDEHLcK6di5rQD6ZFzEpd0fAKCk8jAHSjYTbUskJcZ/wWA01P+7R1l1fr33+XMKKnK4f86AOuuzk3sx8ax/YDQYqXAWsT8vH4s3iSZp/goo46/40sVZ7cZmNzNoSBbbtuXj/uEGBN17NGP0mI4AlJVWc+hQGVFRVpKSo371vn5KVZU/2eQ9JlldVFTEps3f0rt3b9LT0ykvL2fRokUAxMTEMHSof6jnunXryMnJqbdYJHwsWLAg1CGINBid39JYNcpz+/nn/f9++WVIw/gtKitP7HOYklL1aOvWraxYsYIPPvBPmms2mxk/fjzTpk37TUmp++67jzvvvDPwuKysjBYtWjB8+HBiY2N/a9hBZzLV/vBW8vmLxA+5CXdZHuXLZ51wP02u/X9YEpvjPLgZS0orYnpfQkW/qwPD8qp3rySy/dm4C/ey/+nh+Fz+pIk5oRm2zJ64C/fV6i86OgrNOAT4oOabPVS+/T1RE3tjH9oGc+c03BsOAd3BaKTs0QWBKiWsJmyDs6lZthdDnB1zRgIAFVO/wfHKckzpsSR/ekOtXbjWHYIrumOwmYm8pheVr/mrpUxZiXhySsDlxVftz6wbIiy1t92Qi21QFqbMRMwdUgPD94yR/rlg3Btzf3Q8qmYASEysOwdTqIzpcjff5cxlf8kmfPjYW7SOw2W7aR7fgSqXv6pmX9F6vD4vRoORpbv+yxfbpge2b53SO9DuWL0yRvP1zlmkxbamWXw7AA6UbgEgK8lfWfX1zrd5a+WfibTG8dDIBURY6g4tA39l1bGyfqjMqnKVc/+cAbi9NVzR63EGx9Sen67GffRdxGqKoMbjf7yn8Ohw7T6ZYwLD99qk+Odk2lsUvsO5WyV3D1StPTL3PGwFPenX9DZGje5Qq92+fSW0bpNMl65pfPnFDgoL/BciaU1jyD1U+/c1a+Yaxl/RjazsJK66tievv7oKr9dHy8x4AKqrXTzx2Bd4PF7GXtKZvslHM6pOp5vi4ioSEiLo0LEJS77ajclkoH3HuhVvJyspMZEePUcFqnljYmIYNWoUQGAdQNeuXenSpctv3p+ED5fLxYIFCxg2bFigIk6ksdD5LY2Vzu3wdmSE1y9RUqoeTZs2DbfbTXr60eFPPp8Pm83G1KlTiYuL+1X92mw2bDZbnfUWi6VR/OerObCRHbckg8eNz13zyxsAMX2vJObMy/C6nBx66XKiuo4k5bInSb3qeaq2fIm7KIfC9x8m4p4FRLTpT9bzB3Dl78YUk4I5Pp2qbYtxfP/xj3rVfEPHckxfSeQV3TFYzUTfeBYld80h8ppeWNqnkvTO1bh3FWIwmzClx2Kwmck/9994DpbhOVSGqWks0ZP6Yz+/A6Ym0fg83lqvbvW8zbiu7oGlUxqx9w4h8sru4PZiahFPXr+p+FxO3LuLAP+wwaQPJ+KrclE0cTaVM78n4pKumFKjSZp5Je59xZgz/RMpu7blU/Xp5uMcjfyWIcT1rX/2eEZ1nkx5dSFFlQeJsSWRGOV/31yx1z+0rcCRw9c732ZQ6ysZ3/Nhhra7HqfbQVJkMyKsscxYdhcHSrbU6veSM+7nnHY3kBTVHJPRgtNdyaJtMwDYX7KF1im9GJB1Oa1TehMX0eSkEpZH5pKKsMTw+AVLcHtrjpvQyi3fGVh++PzPKa3K493v/8q2vKVsyl1Cx7SBXNL9LwzIvoKEyDTslmiqXRXM2/jSSb2GwXTgmHm0Hho1n8pKJ1HWuknO+XO3knlLApFRVu66dzD5eQ5iYm3s2V3M66+uqtX24P5SXp/+Hdf/vg8dOzXh0su7MXvmmsBQP7vdwp8eGIrb48Fur/t37qtFO7no4s5ktU7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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TASKS = recipe_to_tasks(\n", " [\"A1\", \"A2\", \"A3\", \"A4\"],\n", " [\"Mixer\", \"Reactor\", \"Separator\", \"Packaging\"],\n", " [1, 5, 4, 1.5],\n", ")\n", "results = jobshop(TASKS)\n", "visualize(results)\n", "print(\"Makespan =\", max([task[\"Finish\"] for task in results]))" ] }, { "cell_type": "markdown", "metadata": { "id": "j9-oR-tIshop", "pycharm": {} }, "source": [ "Earlier we found that it took 11.5 hours to produce one batch of product A. As we see here, we can produce a second batch with only 5.0 additional hours because some of the tasks overlap. The overlapping of tasks is the key to gaining efficiency in batch processing facilities.\n", "\n", "Let's next consider production of a single batch each of products A, B, and C." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 665 }, "executionInfo": { "elapsed": 4593, "status": "ok", "timestamp": 1603817905087, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "mnntHP1jshoq", "outputId": "a657efa0-2122-48d5-f3c7-3dd19a55f5e9", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('A', 'Mixer') {'dur': 1, 'prec': None}\n", "('A', 'Reactor') {'dur': 5, 'prec': ('A', 'Mixer')}\n", "('A', 'Separator') {'dur': 4, 'prec': ('A', 'Reactor')}\n", "('A', 'Packaging') {'dur': 1.5, 'prec': ('A', 'Separator')}\n", "('B', 'Separator') {'dur': 4.5, 'prec': None}\n", "('B', 'Packaging') {'dur': 1, 'prec': ('B', 'Separator')}\n", "('C', 'Separator') {'dur': 5, 'prec': None}\n", "('C', 'Reactor') {'dur': 3, 'prec': ('C', 'Separator')}\n", "('C', 'Packaging') {'dur': 1.5, 'prec': ('C', 'Reactor')}\n", "HiGHS 1.5.1: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.1: optimal solution; objective 15\n", "71 simplex iterations\n", "1 branching nodes\n", "Makespan = 15.0\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# update is used to append dictionaries\n", "TASKS = recipeA\n", "TASKS.update(recipeB)\n", "TASKS.update(recipeC)\n", "\n", "for k, v in TASKS.items():\n", " print(k, v)\n", "\n", "results = jobshop(TASKS)\n", "visualize(results)\n", "print(\"Makespan =\", max([task[\"Finish\"] for task in results]))" ] }, { "cell_type": "markdown", "metadata": { "id": "daGJ6KE7shot", "pycharm": {} }, "source": [ "The individual production of A, B, and C required 11.5, 5.5, and 9.5 hours, respectively, for a total of 25.5 hours. As we see here, by scheduling the production simultaneously, we can get all three batches done in just 15 hours. \n", "\n", "As we see below, each additional set of three products takes an additional 13 hours. So there is considerable efficiency gained by scheduling over longer intervals whenever possible." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 566 }, "executionInfo": { "elapsed": 14311, "status": "ok", "timestamp": 1603817914815, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "tmmIFHETshov", "outputId": "189eb366-cb64-4da1-d5ad-0ee5317c8fd4", "pycharm": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HiGHS 1.5.1:HiGHS 1.5.1: optimal solution; objective 28\n", "12290 simplex iterations\n", "622 branching nodes\n", "Makespan = 27.99999999999888\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TASKS = recipe_to_tasks(\n", " [\"A1\", \"A2\"], [\"Mixer\", \"Reactor\", \"Separator\", \"Packaging\"], [1, 5, 4, 1.5]\n", ")\n", "TASKS.update(recipe_to_tasks([\"B1\", \"B2\"], [\"Separator\", \"Packaging\"], [4.5, 1]))\n", "TASKS.update(\n", " recipe_to_tasks([\"C1\", \"C2\"], [\"Separator\", \"Reactor\", \"Packaging\"], [5, 3, 1.5])\n", ")\n", "\n", "results = jobshop(TASKS)\n", "visualize(results)\n", "print(\"Makespan =\", max([task[\"Finish\"] for task in results]))" ] }, { "cell_type": "markdown", "metadata": { "id": "68wdMOQ9sho0", "pycharm": {} }, "source": [ "### Adding time for unit clean out\n", "\n", "A common feature of batch unit operations is a requirement that equipment be cleaned prior to reuse. \n", "\n", "In most cases the time needed for clean out would be specific to the equipment and product. But for the purposes this notebook, we implement can implement a simple clean out policy with a single non-negative parameter $t_{clean} \\geq 0$ which, if specified, requires a period no less than $t_{clean}$ between the finish of one task and the start of another on every piece of equipment.\n", "\n", "This policy is implemented by modifying the usual disjunctive constraints to avoid machine conflicts to read\n", "\n", "$$\n", "\\begin{align}\n", "\\left[\\text{start}_{j,m}+\\text{dur}_{j,m} + t_{clean} \\leq \\text{start}_{k,m}\\right] \\vee \\left[\\text{start}_{k,m}+\\text{dur}_{k,m} + t_{clean} \\leq \\text{start}_{j,m}\\right]\n", "\\end{align}\n", "$$\n", "\n", "For this purpose, we write a new JobShopModel_Clean" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting jobshop_model_clean.mod\n" ] } ], "source": [ "%%writefile jobshop_model_clean.mod\n", "\n", "set JOBS;\n", "set MACHINES;\n", "set TASKS within {JOBS, MACHINES};\n", "set TASKORDER within {TASKS, TASKS};\n", "set DISJUNCTIONS within {JOBS, JOBS, MACHINES};\n", "\n", "param dur{TASKS};\n", "param ub;\n", "\n", "var makespan >= 0, <= ub;\n", "var start{TASKS} >= 0, <= ub;\n", "\n", "minimize minimize_makespan: makespan;\n", "\n", "s.t. finish_tasks {(j,m) in TASKS}:\n", " start[j, m] + dur[j, m] <= makespan;\n", "\n", "s.t. preceding {(j, m, k, n) in TASKORDER}:\n", " start[k, n] + dur[k, n] <= start[j, m];\n", "\n", "s.t. no_overlap {(j, k, m) in DISJUNCTIONS}:\n", " (start[j, m] + dur[j, m] + tclean <= start[k, m]\n", " or\n", " start[k, m] + dur[k, m] + tclean <= start[j, m]);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 566 }, "executionInfo": { "elapsed": 23784, "status": "ok", "timestamp": 1603817924300, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "HjvMPceePHBz", "outputId": "b282c450-7024-47ec-faa2-b428ae65f24e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HiGHS 1.5.1:HiGHS 1.5.1: optimal solution; objective 28\n", "12290 simplex iterations\n", "622 branching nodes\n", "Makespan = 27.99999999999888\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def jobshop_model_clean(TASKS, tclean=0):\n", " m = AMPL()\n", " m.read(\"jobshop_model.mod\")\n", "\n", " jobs = list(set([j for (j, m) in TASKS]))\n", " machines = list(set([m for (j, m) in TASKS]))\n", " tasks = list(TASKS.keys())\n", " taskorder = [\n", " (j, m, k, n)\n", " for (j, m) in tasks\n", " for (k, n) in tasks\n", " if (k, n) == TASKS[(j, m)][\"prec\"]\n", " ]\n", " disjunctions = [\n", " (j, k, m) for (j, m) in tasks for (k, n) in tasks if m == n and j < k\n", " ]\n", " dur = {(j, m): TASKS[(j, m)][\"dur\"] for (j, m) in TASKS}\n", " ub = sum(dur[i] for i in dur)\n", "\n", " m.set[\"JOBS\"] = jobs\n", " m.set[\"MACHINES\"] = machines\n", " m.set[\"TASKS\"] = tasks\n", " m.set[\"TASKORDER\"] = taskorder\n", " m.set[\"DISJUNCTIONS\"] = disjunctions\n", "\n", " m.param[\"dur\"] = dur\n", " m.param[\"ub\"] = ub\n", "\n", " return m\n", "\n", "\n", "model = jobshop_model_clean(TASKS, tclean=0.5)\n", "results = jobshop_solve(model)\n", "visualize(results)\n", "print(\"Makespan =\", max([task[\"Finish\"] for task in results]))" ] }, { "cell_type": "markdown", "metadata": { "id": "HrHUYrQUsho7", "pycharm": {} }, "source": [ "### Adding a zero wait policy\n", "\n", "One of the issues in the use of job shop scheduling for chemical process operations are situations where there it is not possible to store intermediate materials. If there is no way to store intermediates, either in the processing equipment or in external vessels, then a **zero-wait** policy may be required. \n", "\n", "A zero-wait policy requires subsequent processing machines to be available immediately upon completion of any task. To implement this policy, the usual precident sequencing constraint of a job shop scheduling problem, i.e.,\n", "\n", "$$\n", "\\begin{align*}\n", "\\text{start}_{k,n}+\\text{Dur}_{k,n} \\leq \\text{start}_{j,m}\\ \\ \\ \\ \\text{for } (k,n) =\\text{Prec}_{j,m}\n", "\\end{align*}\n", "$$\n", "\n", "is changed to \n", "\n", "$$\n", "\\begin{align*}\n", "\\text{start}_{k,n}+\\text{Dur}_{k,n} = \\text{start}_{j,m}\\ \\ \\ \\ \\text{for } (k,n) =\\text{Prec}_{j,m}\\text{ and ZW is True}\n", "\\end{align*}\n", "$$\n", "\n", "if the zero-wait policy is in effect. \n", "\n", "While this could be implemented on an equipment or product specific basis, here we add an optional ZW flag to the JobShop function that, by default, is set to False." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting jobshop_model_clean_zw.mod\n" ] } ], "source": [ "%%writefile jobshop_model_clean_zw.mod\n", "\n", "set JOBS;\n", "set MACHINES;\n", "set TASKS within {JOBS, MACHINES};\n", "set TASKORDER within {TASKS, TASKS};\n", "set DISJUNCTIONS within {JOBS, JOBS, MACHINES};\n", "\n", "param dur{TASKS};\n", "param ub;\n", "param tclean;\n", "param ZW default 1;\n", "\n", "var makespan >= 0, <= ub;\n", "var start{TASKS} >= 0, <= ub;\n", "\n", "minimize minimize_makespan: makespan;\n", "\n", "s.t. finish_tasks {(j,m) in TASKS}:\n", " start[j, m] + dur[j, m] <= makespan;\n", "\n", "s.t. preceding {(j, m, k, n) in TASKORDER}:\n", " ZW == 1 ==> start[k, n] + dur[k, n] == start[j, m]\n", " else\n", " start[k, n] + dur[k, n] <= start[j, m];\n", " \n", "s.t. disjunctions {(j, k, m) in DISJUNCTIONS}:\n", " (start[j, m] + dur[j, m] + tclean <= start[k, m]\n", " or\n", " start[k, m] + dur[k, m] + tclean <= start[j, m]);" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 566 }, "executionInfo": { "elapsed": 25811, "status": "ok", "timestamp": 1603817926340, "user": { "displayName": "Jeffrey Kantor", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64", "userId": "09038942003589296665" }, "user_tz": 240 }, "id": "owKSMRnHPHB2", "outputId": "495434fa-efb5-4111-8eba-6df5974387cb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HiGHS 1.5.1: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.1: optimal solution; objective 32\n", "1552 simplex iterations\n", "81 branching nodes\n", "Makespan = 32.0\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def jobshop_model_clean_zw(TASKS, tclean=0, ZW=False):\n", " m = AMPL()\n", " m.read(\"jobshop_model_clean_zw.mod\")\n", "\n", " jobs = list(set([j for (j, m) in TASKS]))\n", " machines = list(set([m for (j, m) in TASKS]))\n", " tasks = list(TASKS.keys())\n", " taskorder = [\n", " (j, m, k, n)\n", " for (j, m) in tasks\n", " for (k, n) in tasks\n", " if (k, n) == TASKS[(j, m)][\"prec\"]\n", " ]\n", " disjunctions = [\n", " (j, k, m) for (j, m) in tasks for (k, n) in tasks if m == n and j < k\n", " ]\n", " dur = {(j, m): TASKS[(j, m)][\"dur\"] for (j, m) in TASKS}\n", " ub = sum(dur[i] for i in dur)\n", "\n", " m.set[\"JOBS\"] = jobs\n", " m.set[\"MACHINES\"] = machines\n", " m.set[\"TASKS\"] = tasks\n", " m.set[\"TASKORDER\"] = taskorder\n", " m.set[\"DISJUNCTIONS\"] = disjunctions\n", "\n", " m.param[\"dur\"] = dur\n", " m.param[\"ub\"] = ub\n", " m.param[\"tclean\"] = tclean\n", " m.param[\"ZW\"] = 1 if ZW else 0\n", "\n", " m.option[\"solver\"] = SOLVER\n", "\n", " return m\n", "\n", "\n", "model = jobshop_model_clean_zw(TASKS, tclean=0.5, ZW=True)\n", "results = jobshop_solve(model)\n", "visualize(results)\n", "print(\"Makespan =\", max([task[\"Finish\"] for task in results]))" ] }, { "cell_type": "markdown", "metadata": { "id": "mINloyXxPHB8" }, "source": [ "## References\n", "\n", "* Applegate, David, and William Cook. [\"A computational study of the job-shop scheduling problem.\"](https://doi.org/10.1287/ijoc.3.2.149) ORSA Journal on computing 3, no. 2 (1991): 149-156. [pdf available](http://www.math.uwaterloo.ca/~bico/papers/jobshop.pdf)\n", "* Beasley, John E. [\"OR-Library: distributing test problems by electronic mail.\"](https://www.jstor.org/stable/pdf/2582903.pdf?casa_token=RWUXQ6e2VngAAAAA:g-dwMXjJfCUWqoLJk5y-MpDDhLmJKSGE36YU5kpkXZ71dmos25vntN0RlDFLxDme_QsOw8Oc3oi6juXCkVCfm2gcycCK0SYNoOnOZqRYDp3EqSrXP6nDeQ) Journal of the operational research society 41, no. 11 (1990): 1069-1072. [OR-Library](http://people.brunel.ac.uk/~mastjjb/jeb/info.html)\n", "* Guéret, Christelle, Christian Prins, and Marc Sevaux. [\"Applications of optimization with Xpress-MP.\"](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.69.9634&rep=rep1&type=pdf) contract (1999): 00034.\n", "* Manne, Alan S. [\"On the job-shop scheduling problem.\"](https://doi.org/10.1287/opre.8.2.219) Operations Research 8, no. 2 (1960): 219-223." ] }, { "cell_type": "markdown", "metadata": { "id": "9ubGM7r1PHB8" }, "source": [ "## Exercises" ] }, { "cell_type": "markdown", "metadata": { "id": "gagPe62ZPHB9" }, "source": [ "### Task specific cleanout\n", "\n", "Clean out operations are often slow and time consuming. Further more, the time required to perform a clean out frequently depends on the type of machine, and the task performed by the machine. For this exercise, create a data format to include task-specific clean out times, and model the job shop model to accommodate this additional information." ] }, { "cell_type": "markdown", "metadata": { "id": "g21rYpmhPHB9" }, "source": [ "### Computational impact of a zero-wait policy\n", "\n", "Repeat the benchmark problem calculation, but with a zero-wait policy. Does the execution time increase or descrease as a consequence of specifying zero-wait?" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 }