Reinforcement Learning for Logistics and Supply Chain Management: Methodologies, State of the Art, and Future Opportunities

91 Pages Posted: 5 Oct 2021 Last revised: 19 May 2022

See all articles by Yimo Yan

Yimo Yan

The University of Hong Kong

Andy H.F. Chow

City University of Hong Kong (CityU)

Chin Pang Ho

City University of Hong Kong (CityU)

Yong-Hong Kuo

The University of Hong Kong - Department of Industrial and Manufacturing Systems Engineering

Qihao Wu

The University of Hong Kong

Chengshuo Ying

City University of Hong Kong (CityU)

Date Written: May 9, 2022

Abstract

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. We 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.

Keywords: Reinforcement learning, logistics, supply chain, Markov decision process, Q-learning, Actor-Critic methods, neural network

Suggested Citation

Yan, Yimo and Chow, Andy H.F. and Ho, Chin Pang and Kuo, Yong-Hong and Wu, Qihao and Ying, Chengshuo, Reinforcement Learning for Logistics and Supply Chain Management: Methodologies, State of the Art, and Future Opportunities (May 9, 2022). Available at SSRN: https://ssrn.com/abstract=3935816 or http://dx.doi.org/10.2139/ssrn.3935816

Yimo Yan

The University of Hong Kong ( email )

Pokfulam Road
Hong Kong, Pokfulam HK
China

Andy H.F. Chow

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Chin Pang Ho

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Yong-Hong Kuo (Contact Author)

The University of Hong Kong - Department of Industrial and Manufacturing Systems Engineering ( email )

8/F Haking Wong Building
Pokfulam Road
Hong Kong
China

Qihao Wu

The University of Hong Kong ( email )

Pokfulam Road
Hong Kong, Pokfulam HK
China

Chengshuo Ying

City University of Hong Kong (CityU) ( email )

Centre for Applied One Health Research and Policy
Hong Kong

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
1,114
Abstract Views
2,755
Rank
38,196
PlumX Metrics