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routing: regen notebooks
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‎examples/notebook/contrib/permutation_flow_shop.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "google",
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"metadata": {},
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"source": [
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"##### Copyright 2023 Google LLC."
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]
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},
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{
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"cell_type": "markdown",
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"id": "apache",
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"metadata": {},
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"source": [
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"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"you may not use this file except in compliance with the License.\n",
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"You may obtain a copy of the License at\n",
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"\n",
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" http://www.apache.org/licenses/LICENSE-2.0\n",
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"\n",
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"Unless required by applicable law or agreed to in writing, software\n",
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"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"See the License for the specific language governing permissions and\n",
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"limitations under the License.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "basename",
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"metadata": {},
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"source": [
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"# permutation_flow_shop"
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]
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},
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{
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"cell_type": "markdown",
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"id": "link",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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"<td>\n",
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"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/contrib/permutation_flow_shop.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
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"</td>\n",
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"<td>\n",
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"<a href=\"https://github.com/google/or-tools/blob/main/examples/contrib/permutation_flow_shop.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
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"</td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "doc",
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"metadata": {},
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"source": [
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"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "install",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install ortools"
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]
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},
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{
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"cell_type": "markdown",
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"id": "description",
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"metadata": {},
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"source": [
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"\n",
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"This model implements the permutation flow shop problem (PFSP).\n",
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"\n",
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"In the PFSP, a set of jobs has to be processed on a set of machines. Each job\n",
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"must be processed on each machine in sequence and all jobs have to be processed\n",
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"in the same order on every machine. The objective is to minimize the makespan.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Sequence\n",
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"from dataclasses import dataclass\n",
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"from itertools import product\n",
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"\n",
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"import numpy as np\n",
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"\n",
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"from ortools.sat.colab import flags\n",
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"from google.protobuf import text_format\n",
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"from ortools.sat.python import cp_model\n",
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"\n",
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"_PARAMS = flags.define_string(\n",
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" \"params\",\n",
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" \"num_search_workers:16\",\n",
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" \"Sat solver parameters.\",\n",
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")\n",
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"\n",
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"_TIME_LIMIT = flags.define_float(\n",
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" \"time_limit\",\n",
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" 60.0,\n",
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" \"Time limit in seconds. Default is 60s.\",\n",
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")\n",
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"\n",
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"_LOG = flags.define_boolean(\n",
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" \"log\",\n",
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" False,\n",
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" \"Whether to log the solver output.\",\n",
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")\n",
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"\n",
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"\n",
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"@dataclass\n",
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"class TaskType:\n",
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" \"\"\"\n",
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" Small wrapper to hold the start, end, and interval variables of a task.\n",
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" \"\"\"\n",
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"\n",
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" start: cp_model.IntVar\n",
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" end: cp_model.IntVar\n",
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" interval: cp_model.IntervalVar\n",
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"\n",
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"\n",
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"def permutation_flow_shop(\n",
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" processing_times: np.ndarray,\n",
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" time_limit: float,\n",
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" log: bool,\n",
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" params: str\n",
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"):\n",
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" \"\"\"\n",
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" Solves the given permutation flow shop problem instance with OR-Tools.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" processing_times\n",
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" An n-by-m matrix of processing times of the jobs on the machines.\n",
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" time_limit\n",
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" The time limit in seconds. If not set, the solver runs until an\n",
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" optimal solution is found.\n",
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" log\n",
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" Whether to log the solver output. Default is False.\n",
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"\n",
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" Raises\n",
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" ------\n",
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" ValueError\n",
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" If the number of lines is greater than 1, i.e., the instance is a\n",
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" distributed permutation flow shop problem.\n",
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" \"\"\"\n",
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" m = cp_model.CpModel()\n",
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" num_jobs, num_machines = processing_times.shape\n",
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" horizon = processing_times.sum()\n",
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"\n",
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" # Create interval variables for all tasks (each job/machine pair).\n",
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" tasks = {}\n",
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" for job, machine in product(range(num_jobs), range(num_machines)):\n",
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" start = m.new_int_var(0, horizon, \"\")\n",
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" end = m.new_int_var(0, horizon, \"\")\n",
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" duration = processing_times[job][machine]\n",
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" interval = m.new_interval_var(start, duration, end, \"\")\n",
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" tasks[job, machine] = TaskType(start, end, interval)\n",
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"\n",
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" # No overlap for all job intervals on this machine.\n",
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" for machine in range(num_machines):\n",
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" intervals = [tasks[job, machine].interval for job in range(num_jobs)]\n",
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" m.add_no_overlap(intervals)\n",
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"\n",
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" # Add precedence constraints between tasks of the same job.\n",
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" for job, machine in product(range(num_jobs), range(num_machines - 1)):\n",
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" pred = tasks[job, machine]\n",
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" succ = tasks[job, machine + 1]\n",
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" m.add(pred.end <= succ.start)\n",
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"\n",
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" # Create arcs for circuit constraints.\n",
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" arcs = []\n",
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" for idx1 in range(num_jobs):\n",
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" arcs.append((0, idx1 + 1, m.new_bool_var(\"start\")))\n",
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" arcs.append((idx1 + 1, 0, m.new_bool_var(\"end\")))\n",
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"\n",
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" lits = {}\n",
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" for idx1, idx2 in product(range(num_jobs), repeat=2):\n",
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" if idx1 != idx2:\n",
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" lit = m.new_bool_var(f\"{idx1} -> {idx2}\")\n",
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" lits[idx1, idx2] = lit\n",
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" arcs.append((idx1 + 1, idx2 + 1, lit))\n",
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"\n",
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" m.add_circuit(arcs)\n",
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"\n",
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" # Enforce that the permutation of jobs is the same on all machines.\n",
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" for machine in range(num_machines):\n",
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" starts = [tasks[job, machine].start for job in range(num_jobs)]\n",
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" ends = [tasks[job, machine].end for job in range(num_jobs)]\n",
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"\n",
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" for idx1, idx2 in product(range(num_jobs), repeat=2):\n",
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" if idx1 == idx2:\n",
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" continue\n",
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"\n",
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" # Since all machines share the same arc literals, if the literal\n",
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" # i -> j is True, this enforces that job i is always scheduled\n",
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" # before job j on all machines.\n",
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" lit = lits[idx1, idx2]\n",
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" m.add(ends[idx1] <= starts[idx2]).only_enforce_if(lit)\n",
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"\n",
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" # Set minimizing makespan as objective.\n",
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" obj_var = m.new_int_var(0, horizon, \"makespan\")\n",
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" completion_times = [\n",
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" tasks[(job, num_machines - 1)].end for job in range(num_jobs)\n",
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" ]\n",
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" m.add_max_equality(obj_var, completion_times)\n",
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" m.minimize(obj_var)\n",
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"\n",
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" solver = cp_model.CpSolver()\n",
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" if params:\n",
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" text_format.Parse(params, solver.parameters)\n",
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" solver.parameters.log_search_progress = log\n",
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" solver.parameters.max_time_in_seconds = time_limit\n",
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"\n",
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" status_code = solver.Solve(m)\n",
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" status = solver.StatusName(status_code)\n",
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"\n",
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" print(f\"Status: {status}\")\n",
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" print(f\"Makespan: {solver.ObjectiveValue()}\")\n",
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"\n",
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" if status in [\"OPTIMAL\", \"FEASIBLE\"]:\n",
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" start = [solver.Value(tasks[job, 0].start) for job in range(num_jobs)]\n",
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" solution = np.argsort(start) + 1\n",
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" print(f\"Solution: {solution}\")\n",
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"\n",
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"\n",
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"def main(argv: Sequence[str]) -> None:\n",
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" \"\"\"Creates the data and calls the solving procedure.\"\"\"\n",
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" # VRF_10_5_2 instance from http://soa.iti.es/problem-instances.\n",
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" # Optimal makespan is 698.\n",
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" processing_times = [\n",
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" [79, 67, 10, 48, 52],\n",
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" [40, 40, 57, 21, 54],\n",
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" [48, 93, 49, 11, 79],\n",
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" [16, 23, 19, 2, 38],\n",
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" [38, 90, 57, 73, 3],\n",
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" [76, 13, 99, 98, 55],\n",
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" [73, 85, 40, 20, 85],\n",
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" [34, 6, 27, 53, 21],\n",
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" [38, 6, 35, 28, 44],\n",
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" [32, 11, 11, 34, 27],\n",
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" ]\n",
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"\n",
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" permutation_flow_shop(\n",
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" np.array(processing_times), _TIME_LIMIT.value, _LOG.value, _PARAMS.value\n",
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" )\n",
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"\n",
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"\n",
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"app.run(main) \n",
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"\n"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎examples/notebook/constraint_solver/cvrp_reload.ipynb renamed to ‎examples/notebook/routing/cvrp_reload.ipynb

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"source": [
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"<table align=\"left\">\n",
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"<td>\n",
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"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/constraint_solver/cvrp_reload.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
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"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/routing/cvrp_reload.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
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"</td>\n",
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"<td>\n",
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"<a href=\"https://github.com/google/or-tools/blob/main/ortools/constraint_solver/samples/cvrp_reload.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
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"<a href=\"https://github.com/google/or-tools/blob/main/ortools/routing/samples/cvrp_reload.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
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"</td>\n",
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"</table>"
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]
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"source": [
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"from functools import partial\n",
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"\n",
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"from ortools.constraint_solver import pywrapcp\n",
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"from ortools.routing import enums_pb2\n",
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"from ortools.routing import pywraprouting\n",
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"\n",
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"\n",
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"###########################\n",
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" data = create_data_model()\n",
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"\n",
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" # Create the routing index manager\n",
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" manager = pywrapcp.RoutingIndexManager(\n",
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" manager = pywraprouting.RoutingIndexManager(\n",
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" data[\"num_locations\"], data[\"num_vehicles\"], data[\"depot\"]\n",
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" )\n",
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"\n",
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" # Create Routing Model\n",
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" routing = pywrapcp.RoutingModel(manager)\n",
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" routing = pywraprouting.RoutingModel(manager)\n",
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"\n",
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" # Define weight of each edge\n",
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" distance_evaluator_index = routing.RegisterTransitCallback(\n",
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" add_time_window_constraints(routing, manager, data, time_evaluator_index)\n",
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"\n",
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" # Setting first solution heuristic (cheapest addition).\n",
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" search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
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" search_parameters = pywraprouting.DefaultRoutingSearchParameters()\n",
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" search_parameters.first_solution_strategy = (\n",
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" enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
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" ) # pylint: disable=no-member\n",

‎examples/notebook/constraint_solver/cvrptw_break.ipynb renamed to ‎examples/notebook/routing/cvrptw_break.ipynb

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"source": [
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"<table align=\"left\">\n",
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"<td>\n",
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"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/constraint_solver/cvrptw_break.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
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"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/routing/cvrptw_break.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
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"</td>\n",
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"<td>\n",
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"<a href=\"https://github.com/google/or-tools/blob/main/ortools/constraint_solver/samples/cvrptw_break.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
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"<a href=\"https://github.com/google/or-tools/blob/main/ortools/routing/samples/cvrptw_break.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
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"</td>\n",
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"</table>"
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]
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"outputs": [],
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"source": [
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"import functools\n",
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"from ortools.constraint_solver import pywrapcp\n",
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"from ortools.routing import enums_pb2\n",
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"from ortools.routing import pywraprouting\n",
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"\n",
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"\n",
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"def create_data_model():\n",
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" data = create_data_model()\n",
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"\n",
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" # Create the routing index manager\n",
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" manager = pywrapcp.RoutingIndexManager(\n",
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" manager = pywraprouting.RoutingIndexManager(\n",
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" data[\"numlocations_\"], data[\"num_vehicles\"], data[\"depot\"]\n",
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" )\n",
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"\n",
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" # Create Routing Model\n",
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" routing = pywrapcp.RoutingModel(manager)\n",
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" routing = pywraprouting.RoutingModel(manager)\n",
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"\n",
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" # Define weight of each edge\n",
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" distance_evaluator_index = routing.RegisterTransitCallback(\n",
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" )\n",
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"\n",
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" # Setting first solution heuristic (cheapest addition).\n",
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" search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
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" search_parameters = pywraprouting.DefaultRoutingSearchParameters()\n",
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" search_parameters.first_solution_strategy = (\n",
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" enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
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" ) # pylint: disable=no-member\n",

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