250 lines
9.3 KiB
Plaintext
250 lines
9.3 KiB
Plaintext
{
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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 2025 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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"# vrp_time_windows"
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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/constraint_solver/vrp_time_windows.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/vrp_time_windows.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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"Vehicles Routing Problem (VRP) with Time Windows.\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 ortools.constraint_solver import routing_enums_pb2\n",
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"from ortools.constraint_solver import pywrapcp\n",
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"\n",
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"\n",
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"\n",
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"def create_data_model():\n",
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" \"\"\"Stores the data for the problem.\"\"\"\n",
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" data = {}\n",
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" data[\"time_matrix\"] = [\n",
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" [0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7],\n",
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" [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14],\n",
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" [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9],\n",
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" [8, 3, 11, 0, 1, 7, 10, 6, 10, 10, 14, 6, 7, 9, 14, 6, 16],\n",
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" [7, 2, 10, 1, 0, 6, 9, 4, 8, 9, 13, 4, 6, 8, 12, 8, 14],\n",
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" [3, 6, 6, 7, 6, 0, 2, 3, 2, 2, 7, 9, 7, 7, 6, 12, 8],\n",
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" [6, 8, 3, 10, 9, 2, 0, 6, 2, 5, 4, 12, 10, 10, 6, 15, 5],\n",
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" [2, 4, 9, 6, 4, 3, 6, 0, 4, 4, 8, 5, 4, 3, 7, 8, 10],\n",
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" [3, 8, 5, 10, 8, 2, 2, 4, 0, 3, 4, 9, 8, 7, 3, 13, 6],\n",
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" [2, 8, 8, 10, 9, 2, 5, 4, 3, 0, 4, 6, 5, 4, 3, 9, 5],\n",
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" [6, 13, 4, 14, 13, 7, 4, 8, 4, 4, 0, 10, 9, 8, 4, 13, 4],\n",
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" [6, 7, 15, 6, 4, 9, 12, 5, 9, 6, 10, 0, 1, 3, 7, 3, 10],\n",
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" [4, 5, 14, 7, 6, 7, 10, 4, 8, 5, 9, 1, 0, 2, 6, 4, 8],\n",
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" [4, 8, 13, 9, 8, 7, 10, 3, 7, 4, 8, 3, 2, 0, 4, 5, 6],\n",
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" [5, 12, 9, 14, 12, 6, 6, 7, 3, 3, 4, 7, 6, 4, 0, 9, 2],\n",
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" [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9],\n",
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" [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0],\n",
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" ]\n",
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" data[\"time_windows\"] = [\n",
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" (0, 5), # depot\n",
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" (7, 12), # 1\n",
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" (10, 15), # 2\n",
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" (16, 18), # 3\n",
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" (10, 13), # 4\n",
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" (0, 5), # 5\n",
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" (5, 10), # 6\n",
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" (0, 4), # 7\n",
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" (5, 10), # 8\n",
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" (0, 3), # 9\n",
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" (10, 16), # 10\n",
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" (10, 15), # 11\n",
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" (0, 5), # 12\n",
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" (5, 10), # 13\n",
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" (7, 8), # 14\n",
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" (10, 15), # 15\n",
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" (11, 15), # 16\n",
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" ]\n",
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" data[\"num_vehicles\"] = 4\n",
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" data[\"depot\"] = 0\n",
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" return data\n",
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"\n",
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"\n",
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"def print_solution(data, manager, routing, solution):\n",
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" \"\"\"Prints solution on console.\"\"\"\n",
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" print(f\"Objective: {solution.ObjectiveValue()}\")\n",
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" time_dimension = routing.GetDimensionOrDie(\"Time\")\n",
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" total_time = 0\n",
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" for vehicle_id in range(data[\"num_vehicles\"]):\n",
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" if not routing.IsVehicleUsed(solution, vehicle_id):\n",
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" continue\n",
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" index = routing.Start(vehicle_id)\n",
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" plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
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" while not routing.IsEnd(index):\n",
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" time_var = time_dimension.CumulVar(index)\n",
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" plan_output += (\n",
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" f\"{manager.IndexToNode(index)}\"\n",
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" f\" Time({solution.Min(time_var)},{solution.Max(time_var)})\"\n",
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" \" -> \"\n",
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" )\n",
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" index = solution.Value(routing.NextVar(index))\n",
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" time_var = time_dimension.CumulVar(index)\n",
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" plan_output += (\n",
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" f\"{manager.IndexToNode(index)}\"\n",
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" f\" Time({solution.Min(time_var)},{solution.Max(time_var)})\\n\"\n",
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" )\n",
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" plan_output += f\"Time of the route: {solution.Min(time_var)}min\\n\"\n",
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" print(plan_output)\n",
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" total_time += solution.Min(time_var)\n",
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" print(f\"Total time of all routes: {total_time}min\")\n",
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"\n",
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"\n",
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"def main():\n",
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" \"\"\"Solve the VRP with time windows.\"\"\"\n",
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" # Instantiate the data problem.\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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" len(data[\"time_matrix\"]), 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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"\n",
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" # Create and register a transit callback.\n",
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" def time_callback(from_index, to_index):\n",
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" \"\"\"Returns the travel time between the two nodes.\"\"\"\n",
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" # Convert from routing variable Index to time matrix NodeIndex.\n",
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" from_node = manager.IndexToNode(from_index)\n",
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" to_node = manager.IndexToNode(to_index)\n",
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" return data[\"time_matrix\"][from_node][to_node]\n",
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"\n",
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" transit_callback_index = routing.RegisterTransitCallback(time_callback)\n",
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"\n",
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" # Define cost of each arc.\n",
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" routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
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"\n",
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" # Add Time Windows constraint.\n",
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" time = \"Time\"\n",
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" routing.AddDimension(\n",
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" transit_callback_index,\n",
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" 30, # allow waiting time\n",
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" 30, # maximum time per vehicle\n",
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" False, # Don't force start cumul to zero.\n",
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" time,\n",
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" )\n",
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" time_dimension = routing.GetDimensionOrDie(time)\n",
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" # Add time window constraints for each location except depot.\n",
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" for location_idx, time_window in enumerate(data[\"time_windows\"]):\n",
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" if location_idx == data[\"depot\"]:\n",
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" continue\n",
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" index = manager.NodeToIndex(location_idx)\n",
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" time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])\n",
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" # Add time window constraints for each vehicle start node.\n",
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" depot_idx = data[\"depot\"]\n",
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" for vehicle_id in range(data[\"num_vehicles\"]):\n",
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" index = routing.Start(vehicle_id)\n",
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" time_dimension.CumulVar(index).SetRange(\n",
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" data[\"time_windows\"][depot_idx][0], data[\"time_windows\"][depot_idx][1]\n",
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" )\n",
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"\n",
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" # Instantiate route start and end times to produce feasible times.\n",
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" for i in range(data[\"num_vehicles\"]):\n",
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" routing.AddVariableMinimizedByFinalizer(\n",
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" time_dimension.CumulVar(routing.Start(i))\n",
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" )\n",
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" routing.AddVariableMinimizedByFinalizer(time_dimension.CumulVar(routing.End(i)))\n",
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"\n",
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" # Setting first solution heuristic.\n",
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" search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
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" search_parameters.first_solution_strategy = (\n",
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" routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
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" )\n",
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"\n",
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" # Solve the problem.\n",
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" solution = routing.SolveWithParameters(search_parameters)\n",
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"\n",
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" # Print solution on console.\n",
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" if solution:\n",
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" print_solution(data, manager, routing, solution)\n",
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"\n",
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"\n",
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"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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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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