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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
205-213

Deep reinforcement learning approach for solving takeout delivery problem

Zhang Xuyang
Liu Yong
Ma Liang
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0179

Abstract

This paper took the minimization of the rider's cost-benefit ratio as the optimization objective and used the minimum ratio traveling salesman problem to model the takeout delivery problem. Aiming at the issues of low accuracy and poor stability of current algorithms for solving this problem, this paper proposed a DRL-MFA algorithm based on deep reinforcement learning. Firstly, the algorithm defined the takeout delivery problem as a Markov decision model to simulate the process between agent and environment. Secondly, the algorithm used a multi-feature aggregation embedding sublayer in the encoder to achieve the advantageous complementarity among the features and improve the modelling ability of nonlinear problems. Finally, the algorithm calculated the probability distribution of the solution by the attention mechanism and pointer network in the decoder and used the strategy gradient to train the network. Through the experimental analysis of classic examples and simu-lation cases in Changchun, the results show that the proposed algorithm can effectively solve the takeout delivery problem, and has higher stability and accuracy than other heuristic algorithms. In addition, this paper conducted the sensitivity experiment to explore the impact of different pricing strategies on takeout delivery, which makes the research more realistic and practical.

Foundation Support

教育部人文社会科学研究青年基金资助项目(21YJC630087)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0179
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 205-213
Serial Number: 1001-3695(2025)01-028-0205-09

Publish History

[2025-01-05] Printed Article

Cite This Article

张旭阳, 刘勇, 马良. 求解外卖配送问题的深度强化学习算法 [J]. 计算机应用研究, 2025, 42 (1): 205-213. (Zhang Xuyang, Liu Yong, Ma Liang. Deep reinforcement learning approach for solving takeout delivery problem [J]. Application Research of Computers, 2025, 42 (1): 205-213. )

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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