A Deep Reinforcement Learning Approach for Crowdshipping Vehicle Routing Problem

Hong Huang, Yu Sheng Lin, Jia Rong Kang, Chun Cheng Lin*

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Extending the vehicle routing problem (VRP), the crowdshipping VRP (CVRP) considers crowdsourcing logistics. Crowdsourcing is flexible and convenient to reduce transportation costs and carbon emissions. However, crowdshipping requires to adapt to real-time changes such as road conditions and customer demands, which heuristic algorithms are not suitable for addressing these issues. Therefore, this study proposes a deep reinforcement learning (DRL) approach to react to real-time environmental changes to solve the CVRP. The CVRP considers a single depot and multiple transfer points to serve multiple customers, in which cargos can be delivered by either the vehicle directly, or crowdworkers after the vehicle stores cargos at transfer points. In the proposed DRL, the agent explores feasible decisions, and revises the path that it should take based on feedbacks. The cost effectiveness that affects crowdshipping includes the vehicle routing, and whether the concerned customer is suitable for crowdshipping. The experimental results show the efficiency and accuracy of the trained model for medium-sized VRPs are much higher than classical heuristic algorithms.

原文English
主出版物標題IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
發行者IEEE Computer Society
頁面598-599
頁數2
ISBN(電子)9781665486873
DOIs
出版狀態Published - 2022
事件2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 - Kuala Lumpur, Malaysia
持續時間: 7 12月 202210 12月 2022

出版系列

名字IEEE International Conference on Industrial Engineering and Engineering Management
2022-December
ISSN(列印)2157-3611
ISSN(電子)2157-362X

Conference

Conference2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
國家/地區Malaysia
城市Kuala Lumpur
期間7/12/2210/12/22

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