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

Citations

Cited by:

1. Ying, Chengshuo & Chow, Andy H.F. & Yan, Yimo & Kuo, Yong-Hong & Wang, Shouyang, 2024. "Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 188(C).
2. Kahalimoghadam, Masoud & Thompson, Russell G. & Rajabifard, Abbas, 2025. "An intelligent multi-agent system for last-mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 200(C).
3. Yang, Xin & Cao, Wenjie & Wang, Kai & Yin, Haodong & Wu, Jianjun & Wu, Lingxiao, 2025. "Integrated scheduling of truck and drone fleets for cargo transportation in post-disaster relief: A two-stage stochastic optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 196(C).
4. Yan, Yimo & Deng, Yang & Cui, Songyi & Kuo, Yong-Hong & Chow, Andy H.F. & Ying, Chengshuo, 2023. "A policy gradient approach to solving dynamic assignment problem for on-site service delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
5. Amine Masmoudi, M. & Mancini, Simona & Baldacci, Roberto & Kuo, Yong-Hong, 2022. "Vehicle routing problems with drones equipped with multi-package payload compartments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
6. Neamatian Monemi, Rahimeh & Gelareh, Shahin & Maculan, Nelson, 2023. "A machine learning based branch-cut-and-Benders for dock assignment and truck scheduling problem in cross-docks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
7. Aloini, Davide & Benevento, Elisabetta & Dulmin, Riccardo & Guerrazzi, Emanuele & Mininno, Valeria, 2025. "Unlocking Real-Time Decision-Making in Warehouses: A machine learning-based forecasting and alerting system for cycle time prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
8. Wang, Haibo & Alidaee, Bahram, 2023. "White-glove service delivery: A quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
9. Wu, Weitiao & Zhu, Yanchen & Liu, Ronghui, 2024. "Dynamic scheduling of flexible bus services with hybrid requests and fairness: Heuristics-guided multi-agent reinforcement learning with imitation learning," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
10. Ho, G.T.S. & Tang, Yuk Ming & Leung, Eric K.H. & Tong, P.H., 2025. "Integrated reinforcement learning of automated guided vehicles dynamic path planning for smart logistics and operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 196(C).
11. Li, Huanhuan & Jiao, Hang & Yang, Zaili, 2023. "AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
12. Fang, Chao & Han, Zonglei & Wang, Wei & Zio, Enrico, 2023. "Routing UAVs in landslides Monitoring: A neural network heuristic for team orienteering with mandatory visits," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
13. Siying Xu & Gaoyu Zhang & Xianzhi Yuan, 2024. "An Enterprise Multi-agent Model with Game Q-Learning Based on a Single Decision Factor," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2523-2562, October.
14. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
15. Tang, Tao & Chai, Simin & Wu, Wei & Yin, Jiateng & D’Ariano, Andrea, 2025. "A multi-task deep reinforcement learning approach to real-time railway train rescheduling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
16. Dubey, Rameshwar & Gunasekaran, Angappa & Papadopoulos, Thanos, 2024. "Benchmarking operations and supply chain management practices using Generative AI: Towards a theoretical framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
17. Ding, Yida & Wandelt, Sebastian & Wu, Guohua & Xu, Yifan & Sun, Xiaoqian, 2023. "Towards efficient airline disruption recovery with reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
18. Liu, Xuan & Zhou, Min & Dong, Hairong, 2025. "Joint rescheduling for timetable and platform assignment of High-Speed Railways via graph neural network-based Deep Reinforcement Learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 202(C).
19. Guo, Feng & Wei, Qu & Wang, Miao & Guo, Zhaoxia & Wallace, Stein W., 2023. "Deep attention models with dimension-reduction and gate mechanisms for solving practical time-dependent vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
20. Li, Chengkun & Cariou, Pierre & Yang, Dong, 2025. "Does voluntary carbon disclosure lead to supply chain leakage: evidence from U.S. Firms’ container carbon emissions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 202(C).
21. Ni, Hsiao-Ping & Liu, Chi-Yun & Paul, Fermodelie & Chong, Wai Oswald & Chou, Jui-Sheng, 2025. "Enhancing supply chain resilience and efficiency of HVAC systems in semiconductor manufacturing facilities using graph-based large multimodal models," Applied Energy, Elsevier, vol. 398(C).
22. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2022. "A Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity: An MDP Model and Dynamic Policy for Post-Decision State Rollout Algorithm in Reinforcement Learning," Mathematics, MDPI, vol. 10(15), pages 1-70, July.
23. Stranieri, Francesco & Fadda, Edoardo & Stella, Fabio, 2024. "Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem," International Journal of Production Economics, Elsevier, vol. 268(C).
24. Hu, Junkai & Xia, Li & Huang, Teng & Wu, Haoran, 2025. "A multi-agent deep reinforcement learning approach for multi-echelon inventory optimization and its application to the beer game," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 203(C).
25. Bootaki, Behrang & Zhang, Guoqing, 2024. "A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology," International Journal of Production Economics, Elsevier, vol. 275(C).
26. Li, Huanhuan & Xing, Wenbin & Jiao, Hang & Yang, Zaili & Li, Yan, 2024. "Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
27. Sun, Xuting & Kuo, Yong-Hong & Xue, Weili & Li, Yanzhi, 2024. "Technology-driven logistics and supply chain management for societal impacts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
28. Kuo, Yong-Hong & Leung, Janny M.Y. & Yan, Yimo, 2023. "Public transport for smart cities: Recent innovations and future challenges," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1001-1026.
29. Winkelmann, Jonas & Spinler, Stefan & Neukirchen, Thomas, 2024. "Green transport fleet renewal using approximate dynamic programming: A case study in German heavy-duty road transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).