IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v298y2022i2p401-412.html
   My bibliography  Save this article

Deep reinforcement learning for inventory control: A roadmap

Author

Listed:
  • Boute, Robert N.
  • Gijsbrechts, Joren
  • van Jaarsveld, Willem
  • Vanvuchelen, Nathalie

Abstract

Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management.

Suggested Citation

  • Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
    • Handle: RePEc:eee:ejores:v:298:y:2022:i:2:p:401-412
    DOI: 10.1016/j.ejor.2021.07.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221721006111
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2021.07.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yingdong Lu & Jing-Sheng Song, 2005. "Order-Based Cost Optimization in Assemble-to-Order Systems," Operations Research, INFORMS, vol. 53(1), pages 151-169, February.
    2. Zhongsheng Hua & Yimin Yu & Wei Zhang & Xiaoyan Xu, 2015. "Structural properties of the optimal policy for dual-sourcing systems with general lead times," IISE Transactions, Taylor & Francis Journals, vol. 47(8), pages 841-850, August.
    3. Hamed Mamani & Shima Nassiri & Michael R. Wagner, 2017. "Closed-Form Solutions for Robust Inventory Management," Management Science, INFORMS, vol. 63(5), pages 1625-1643, May.
    4. Dimitris Bertsimas & Aurélie Thiele, 2006. "A Robust Optimization Approach to Inventory Theory," Operations Research, INFORMS, vol. 54(1), pages 150-168, February.
    5. Tugce Martagan & Ananth Krishnamurthy & Peter A. Leland & Christos T. Maravelias, 2018. "Performance Guarantees and Optimal Purification Decisions for Engineered Proteins," Operations Research, INFORMS, vol. 66(1), pages 18-41, 1-2.
    6. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    7. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    8. Velibor V. Mišić & Georgia Perakis, 2020. "Data Analytics in Operations Management: A Review," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 158-169, January.
    9. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    10. David Simchi-Levi, 2014. "OM Forum —OM Research: From Problem-Driven to Data-Driven Research," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 2-10, February.
    11. Julian Schrittwieser & Ioannis Antonoglou & Thomas Hubert & Karen Simonyan & Laurent Sifre & Simon Schmitt & Arthur Guez & Edward Lockhart & Demis Hassabis & Thore Graepel & Timothy Lillicrap & David , 2020. "Mastering Atari, Go, chess and shogi by planning with a learned model," Nature, Nature, vol. 588(7839), pages 604-609, December.
    12. Kranenburg, A.A. & van Houtum, G.J., 2009. "A new partial pooling structure for spare parts networks," European Journal of Operational Research, Elsevier, vol. 199(3), pages 908-921, December.
    13. Arthur F. Veinott, Jr., 1966. "The Status of Mathematical Inventory Theory," Management Science, INFORMS, vol. 12(11), pages 745-777, July.
    14. Wenbo Chen & Huixiao Yang, 2019. "A heuristic based on quadratic approximation for dual sourcing problem with general lead times and supply capacity uncertainty," IISE Transactions, Taylor & Francis Journals, vol. 51(9), pages 943-956, September.
    15. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    16. Paul Zipkin, 2008. "Old and New Methods for Lost-Sales Inventory Systems," Operations Research, INFORMS, vol. 56(5), pages 1256-1263, October.
    17. David A. Goldberg & Dmitriy A. Katz-Rogozhnikov & Yingdong Lu & Mayank Sharma & Mark S. Squillante, 2016. "Asymptotic Optimality of Constant-Order Policies for Lost Sales Inventory Models with Large Lead Times," Mathematics of Operations Research, INFORMS, vol. 41(3), pages 898-913, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. Goedhart, Joost & Haijema, René & Akkerman, Renzo, 2023. "Modelling the influence of returns for an omni-channel retailer," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1248-1263.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David A. Goldberg & Martin I. Reiman & Qiong Wang, 2021. "A Survey of Recent Progress in the Asymptotic Analysis of Inventory Systems," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1718-1750, June.
    2. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    3. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    4. Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).
    5. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    6. De Moor, Bram J. & Gijsbrechts, Joren & Boute, Robert N., 2022. "Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management," European Journal of Operational Research, Elsevier, vol. 301(2), pages 535-545.
    7. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    8. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
    9. Xie, Chen & Wang, Liangquan & Yang, Chaolin, 2021. "Robust inventory management with multiple supply sources," European Journal of Operational Research, Elsevier, vol. 295(2), pages 463-474.
    10. Jiankun Sun & Jan A. Van Mieghem, 2019. "Robust Dual Sourcing Inventory Management: Optimality of Capped Dual Index Policies and Smoothing," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 912-931, October.
    11. Xiong, Xing & Li, Yanzhi & Yang, Wenguo & Shen, Huaxiao, 2022. "Data-driven robust dual-sourcing inventory management under purchase price and demand uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    12. Bálint Kővári & Lászlo Szőke & Tamás Bécsi & Szilárd Aradi & Péter Gáspár, 2021. "Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission," Sustainability, MDPI, vol. 13(20), pages 1-18, October.
    13. Xiaoyue Li & John M. Mulvey, 2023. "Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network," Papers 2306.08809, arXiv.org.
    14. Dawei Chen & Fangxu Mo & Ye Chen & Jun Zhang & Xinyu You, 2022. "Optimization of Ramp Locations along Freeways: A Dynamic Programming Approach," Sustainability, MDPI, vol. 14(15), pages 1-13, August.
    15. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2021. "A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-44, July.
    16. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    17. van Jaarsveld, Willem & Dollevoet, Twan & Dekker, Rommert, 2015. "Improving spare parts inventory control at a repair shop," Omega, Elsevier, vol. 57(PB), pages 217-229.
    18. Youhua (Frank) Chen & Weili Xue & Jian Yang, 2013. "Technical Note---Optimal Inventory Policy in the Presence of a Long-Term Supplier and a Spot Market," Operations Research, INFORMS, vol. 61(1), pages 88-97, February.
    19. Svoboda, Josef & Minner, Stefan & Yao, Man, 2021. "Typology and literature review on multiple supplier inventory control models," European Journal of Operational Research, Elsevier, vol. 293(1), pages 1-23.
    20. Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:298:y:2022:i:2:p:401-412. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.