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

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https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS136655452200103X;h=repec:eee:transe:v:162:y:2022:i:c:s136655452200103x
Time Added
6/20/2022, 12:20:46 PM
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Data Science
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0
Authors
Yimo Yan Andy H.F. Chow Chin Pang Ho Yong-Hong Kuo Qihao Wu and Chengshuo Ying
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 (search for similar items in EconPapers)
Year Published
2022
Series
Transportation Research Part E: Logistics and Transportation Review 2022 vol. 162 issue C
Rank
0.76
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