A Deep Reinforcement Learning Approach for Crowdshipping Vehicle Routing Problem

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
PublisherIEEE Computer Society
Pages598-599
Number of pages2
ISBN (Electronic)9781665486873
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 - Kuala Lumpur, Malaysia
Duration: 7 Dec 202210 Dec 2022

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2022-December
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

Conference2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period7/12/2210/12/22

Keywords

  • Vehicle routing problem
  • crowdsourcing
  • machine learning

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