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.

    Highlights An introduction to reinforcement learning methodologies is provided. State-of-the-art applications for logistics and supply chain management are reviewed. Current trends of reinforcement learning applications are presented. Future directions for methods and applications are proposed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Yan, Yimo (author) / Chow, Andy H.F. (author) / Ho, Chin Pang (author) / Kuo, Yong-Hong (author) / Wu, Qihao (author) / Ying, Chengshuo (author)


    Publication date :

    2022-04-08




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Concepts & Applications of Logistics & Supply Chain Management

    Moore, D. / Royal Aeronautical Society | British Library Conference Proceedings | 2006




    Hoisting device for logistics supply chain management

    LIU LEI | European Patent Office | 2021

    Free access