158 lines
4.9 KiB
Plaintext
158 lines
4.9 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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"# assignment_min_flow"
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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/graph/assignment_min_flow.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/graph/samples/assignment_min_flow.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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"Linear assignment example."
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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.graph.python import min_cost_flow\n",
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"\n",
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"\n",
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"def main():\n",
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" \"\"\"Solving an Assignment Problem with MinCostFlow.\"\"\"\n",
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" # Instantiate a SimpleMinCostFlow solver.\n",
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" smcf = min_cost_flow.SimpleMinCostFlow()\n",
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"\n",
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" # Define the directed graph for the flow.\n",
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" start_nodes = (\n",
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" [0, 0, 0, 0] + [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4] + [5, 6, 7, 8]\n",
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" )\n",
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" end_nodes = (\n",
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" [1, 2, 3, 4] + [5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8] + [9, 9, 9, 9]\n",
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" )\n",
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" capacities = (\n",
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" [1, 1, 1, 1] + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + [1, 1, 1, 1]\n",
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" )\n",
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" costs = (\n",
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" [0, 0, 0, 0]\n",
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" + [90, 76, 75, 70, 35, 85, 55, 65, 125, 95, 90, 105, 45, 110, 95, 115]\n",
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" + [0, 0, 0, 0]\n",
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" )\n",
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"\n",
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" source = 0\n",
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" sink = 9\n",
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" tasks = 4\n",
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" supplies = [tasks, 0, 0, 0, 0, 0, 0, 0, 0, -tasks]\n",
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"\n",
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" # Add each arc.\n",
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" for start_node, end_node, capacity, cost in zip(\n",
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" start_nodes, end_nodes, capacities, costs\n",
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" ):\n",
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" smcf.add_arc_with_capacity_and_unit_cost(start_node, end_node, capacity, cost)\n",
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" # Add node supplies.\n",
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" for idx, supply in enumerate(supplies):\n",
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" smcf.set_node_supply(idx, supply)\n",
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"\n",
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" # Find the minimum cost flow between node 0 and node 10.\n",
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" status = smcf.solve()\n",
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"\n",
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" if status == smcf.OPTIMAL:\n",
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" print(f\"Total cost = {smcf.optimal_cost()}\")\n",
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" for arc in range(smcf.num_arcs()):\n",
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" # Can ignore arcs leading out of source or into sink.\n",
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" if smcf.tail(arc) != source and smcf.head(arc) != sink:\n",
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"\n",
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" # Arcs in the solution have a flow value of 1. Their start and end nodes\n",
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" # give an assignment of worker to task.\n",
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" if smcf.flow(arc) > 0:\n",
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" print(\n",
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" f\"Worker {smcf.tail(arc)} assigned to task {smcf.head(arc)}. \"\n",
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" f\"Cost = {smcf.unit_cost(arc)}\"\n",
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" )\n",
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" else:\n",
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" print(\"There was an issue with the min cost flow input.\")\n",
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" print(f\"Status: {status}\")\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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