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PAN-CAS

Code for AAMAS2024 Paper: Collaborative Deep Reinforcement Learning for Solving Multi-Objective Vehicle Routing Problems

It contains the implementation codes and testing dataset for three multi-objective vehicle routing problems:

  • Bi-objective traveling salesman problem(BiTSP).

  • Bi-objective capability vehicle routing problem(BiCVRP).

  • Tri-objective traveling salesman problem(TriTSP).

This code is heavily based on the POMO repository and PMOCO repository.

Quick Start

  • To train a model, such as BiTSP with 20 nodes, run train_motsp_n20.py in the corresponding folder.

  • To test a model, such as BiTSP with 20 nodes, run test_motsp_n20.py in the corresponding folder.

  • To test a model using CAS, such as BiTSP with 20 nodes, run test_active_search_CAS.py in the corresponding folder.

  • Pretrained models for each problem can be found in the result folder.

  • The testing dataset used in our paper can be found in the test_data folder.

Reference

If our work is helpful for your research, please cite our paper:

@inproceedings{wu2023collaborative,
  title={Collaborative Deep Reinforcement Learning for Solving Multi-Objective Vehicle Routing Problems},
  author={Wu, Yaoxin and Fan, Mingfeng and Cao, Zhiguang and Gao, Ruobin and Hou, Yaqing and Sartoretti, Guillaume},
  booktitle={23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
  year={2023}
}