Publication Type

Journal Article

Version

acceptedVersion

Publication Date

9-2022

Abstract

Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by hand-crafted rules which may limit their performance. In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention based deep architecture as the policy network to guide the selection of next solution. We apply our method to two important routing problems, i.e. travelling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-theart deep learning based approaches. The learned policies are more effective than the traditional hand-crafted ones, and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions and even real-world dataset.

Keywords

Routing, Heuristic algorithms, Vehicle routing, Traveling salesman problems, Training, Task analysis, Search problems

Discipline

OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Neural Networks and Learning Systems

Volume

33

Issue

9

First Page

5057

Last Page

5069

ISSN

2162-237X

Identifier

10.1109/TNNLS.2021.3068828

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TNNLS.2021.3068828

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