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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 10
3013-3019

End-to-end deep reinforcement learning framework for multi-depot vehicle routing problem

Lei Kun1a
Guo Peng1a,1b
Wang Qixin1a
Zhao Wenchao1a
Tang Liansheng2
1. a. School of Mechanical Engineering, b. Technology & Equipment of Rail Transit Operation & Maintenance Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 610031, China
2. School of Economics & Management, Ningbo University of Technology, Ningbo Zhejiang 315211, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0095

Abstract

This paper proposed an end-to-end deep reinforcement learning framework to improve the efficiency of solving the multi-depot vehicle routing problem(MDVRP). This paper modeled a novel formulation of the Markov decision process(MDP) for the MDVRP, including the definitions of its state, action, and reward. Then, this paper exploited an improved graph attention network(GAT) as the encoder to perform feature embedding on the graph representation of MDVRP, and designed a Transformer-based decoder. Meanwhile, it used the improved REINFORCE algorithm to train the proposed encoder-decoder model. Furthermore, the designed encoder-decoder model wasn't bounded by the size of the graph. That was, once the framework was trained, it could be used to solve MDVRP instances with different scales. Finally, the results on randomly generated and published standard instances verified the feasibility and effectiveness of the proposed framework. Significantly, even on solving MDVRP with 100 customer nodes, the trained model takes only two milliseconds on average to obtain a very competitive solution compared with existing methods.

Foundation Support

浙江省高校重大人文社科攻关计划资助项目(2018QN060)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0095
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 10
Section: Algorithm Research & Explore
Pages: 3013-3019
Serial Number: 1001-3695(2022)10-020-3013-07

Publish History

[2022-05-16] Accepted Paper
[2022-10-05] Printed Article

Cite This Article

雷坤, 郭鹏, 王祺欣, 等. 基于end-to-end深度强化学习的多车场车辆路径优化 [J]. 计算机应用研究, 2022, 39 (10): 3013-3019. (Lei Kun, Guo Peng, Wang Qixin, et al. End-to-end deep reinforcement learning framework for multi-depot vehicle routing problem [J]. Application Research of Computers, 2022, 39 (10): 3013-3019. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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