Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities

With advances in technologies, data science techniques, and computing equipment, there has been rapidly increasing interest in the applications of reinforcement learning (RL) to address the challenges resulting from the evolving business and organisational operations in logistics and supply chain management (SCM). This paper aims to provide a comprehensive review of the development and applications of RL techniques in the field of logistics and SCM. The authors first provide an introduction to RL methodologies, followed by a classification of previous research studies by application. The state-of-the-art research is reviewed and the current challenges are discussed. It is found that Q-learning (QL) is the most popular RL approach adopted by these studies and the research on RL for urban logistics is growing in recent years due to the prevalence of E-commerce and last mile delivery. Finally, some potential directions are presented for future research.

• Record URL:
  https://doi.org/10.1016/j.tre.2022.102712
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S136655452200103X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• Supplemental Notes:
  • © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yan, Yimo
  • Chow, Andy H F
  • 0000-0002-2877-357X
  • Ho, Chin Pang
  • Kuo, Yong-Hong
  • 0000-0002-6170-324X
  • Wu, Qihao
  • Ying, Chengshuo
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 102712
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 162
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: Delivery service; Logistics; Machine learning; Markov processes; Neural networks; Supply chain management
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01850464
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2022 9:43AM