Publication Type

Journal Article

Version

acceptedVersion

Publication Date

9-2022

Abstract

We propose a manager-worker framework (the implementation of our model is publically available at: https://github.com/zcaicaros/manager-worker-mtsptwr) based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e. multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances.

Keywords

Travelling Salesman Problem, Graph Neural Network, Deep Reinforcement Learning

Discipline

Artificial Intelligence and Robotics | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Intelligent Transportation Systems

Volume

24

Issue

1

First Page

1325

Last Page

1336

ISSN

1524-9050

Identifier

10.1109/TITS.2022.3207011

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TITS.2022.3207011

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