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.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 7f44addf40977a20ba6f0964f170c40a
+tags: 645f666f9bcd5a90fca523b33c5a78b7

.doctrees/nbsphinx/examples/00-Getting-Started.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting Started to Job Shop Lib\n",
+    "\n",
+    "The main class of the library is the `JobShopInstance` class, which stores a list of jobs and its `Operations`.\n",
+    "\n",
+    "Each operation is also a class, which stores the machine(s) in which the operation can be processed and its duration (also known as processing time). Let's see an example of how to use the `JobShopInstance` class to model a JSSP instance.\n",
+    "\n",
+    "In this example, we model a simple Job Shop Scheduling Problem using the `JobShopInstance` class. We define three types of machines: CPU, GPU, and Data Center, each represented by a unique identifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobShopInstance(name=Example, num_jobs=3, num_machines=3)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from job_shop_lib import JobShopInstance, Operation\n",
+    "\n",
+    "CPU = 0\n",
+    "GPU = 1\n",
+    "DATA_CENTER = 2\n",
+    "\n",
+    "job_1 = [Operation(CPU, 1), Operation(GPU, 1), Operation(DATA_CENTER, 7)]\n",
+    "job_2 = [Operation(GPU, 5), Operation(DATA_CENTER, 1), Operation(CPU, 1)]\n",
+    "job_3 = [Operation(DATA_CENTER, 1), Operation(CPU, 3), Operation(GPU, 2)]\n",
+    "\n",
+    "jobs = [job_1, job_2, job_3]\n",
+    "\n",
+    "instance = JobShopInstance(\n",
+    "    jobs,\n",
+    "    name=\"Example\",\n",
+    "    # Any extra parameters are stored inside the\n",
+    "    # metadata attribute as a dictionary:\n",
+    "    lower_bound=7,\n",
+    ")\n",
+    "instance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The job and its position in it are automatically inferred. Now, we can access to some stats of the instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of jobs: 3\n",
+      "Number of machines: 3\n",
+      "Number of operations: 9\n",
+      "Name: Example\n",
+      "Is flexible?: False\n",
+      "Max operation time: 7\n",
+      "Machine loads: [5, 8, 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of jobs:\", instance.num_jobs)\n",
+    "print(\"Number of machines:\", instance.num_machines)\n",
+    "print(\"Number of operations:\", instance.num_operations)\n",
+    "print(\"Name:\", instance.name)\n",
+    "print(\"Is flexible?:\", instance.is_flexible)\n",
+    "print(\"Max operation time:\", instance.max_duration)\n",
+    "print(\"Machine loads:\", instance.machine_loads)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 7],\n",
+       "       [5, 1, 1],\n",
+       "       [1, 3, 2]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.array(instance.durations_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [1, 2, 0],\n",
+       "       [2, 0, 1]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array(instance.machines_matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some of this attributes could take $O(num\\_operations)$ to compute. This is the reason we use the `functools.cached_property` decorator to cache the results of the computation of these attributes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we just had to specify the machines in which the operation can be processed and its duration. The `job_id` and the position of the operation in the job are automatically inferred by the `JobShopInstance` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Machine id: 0\n",
+      "Duration: 1\n",
+      "Job id: 0\n",
+      "Position: 0\n",
+      "Operation id: 0\n",
+      "String representation: O(m=0, d=1, j=0, p=0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "first_operation = job_1[0]\n",
+    "print(\"Machine id:\", first_operation.machine_id)\n",
+    "print(\"Duration:\", first_operation.duration)\n",
+    "# If the operation only has one machine, we can use the `machine_id` property\n",
+    "# instead of the `machines` attribute:\n",
+    "print(\"Job id:\", first_operation.job_id)\n",
+    "print(\"Position:\", first_operation.position_in_job)\n",
+    "print(\"Operation id:\", first_operation.operation_id)\n",
+    "print(\"String representation:\", str(first_operation))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "job-shop-lib-gOF0HMZJ-py3.11",
+   "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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}