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Augmenting Monte Carlo Tree Search for managing service level agreements

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  • Fadaki, Masih
  • Asadikia, Atie

Abstract

Monte Carlo Tree Search (MCTS) is an algorithmic technique utilized in reinforcement learning, a subfield of artificial intelligence, that combines tree-based search and random sampling for decision-making in uncertain environments. Although MCTS has been successfully used for playing complex games such as Chess and Go, without customizing the original algorithm using domain knowledge, it struggles to effectively solve complex supply chain problems. This study proposes several augmenting mechanisms for MCTS, tailored for managing service level agreements. Furthermore, we enhance the proposed solution for products/services where adjusting the base-stock level is feasible. The results demonstrate that even with non-stationary demand, where most optimization methods reach their limits, employing these augmentation mechanisms significantly improves MCTS performance.

Suggested Citation

  • Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:proeco:v:271:y:2024:i:c:s092552732400063x
    DOI: 10.1016/j.ijpe.2024.109206
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    References listed on IDEAS

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    1. Kloos, Konstantin & Pibernik, Richard, 2020. "Allocation planning under service-level contracts," European Journal of Operational Research, Elsevier, vol. 280(1), pages 203-218.
    2. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    3. Wang, Yihua & Minner, Stefan, 2024. "Deep reinforcement learning for demand fulfillment in online retail," International Journal of Production Economics, Elsevier, vol. 269(C).
    4. Kaynov, Illya & van Knippenberg, Marijn & Menkovski, Vlado & van Breemen, Albert & van Jaarsveld, Willem, 2024. "Deep Reinforcement Learning for One-Warehouse Multi-Retailer inventory management," International Journal of Production Economics, Elsevier, vol. 267(C).
    5. Hamed Jahani & Babak Abbasi & Zahra Hosseinifard & Masih Fadaki & James P. Minas, 2021. "Disruption risk management in service-level agreements," International Journal of Production Research, Taylor & Francis Journals, vol. 59(1), pages 226-244, January.
    6. Masih Fadaki & Babak Abbasi & Prem Chhetri, 2022. "Quantum game approach for capacity allocation decisions under strategic reasoning," Computational Management Science, Springer, vol. 19(3), pages 491-512, July.
    7. Elena Katok & Douglas Thomas & Andrew Davis, 2008. "Inventory Service-Level Agreements as Coordination Mechanisms: The Effect of Review Periods," Manufacturing & Service Operations Management, INFORMS, vol. 10(4), pages 609-624, June.
    8. Chun†Miin (Jimmy) Chen & Douglas J. Thomas, 2018. "Inventory Allocation in the Presence of Service†Level Agreements," Production and Operations Management, Production and Operations Management Society, vol. 27(3), pages 553-577, March.
    9. Deniz Preil & Michael Krapp, 2022. "Artificial intelligence-based inventory management: a Monte Carlo tree search approach," Annals of Operations Research, Springer, vol. 308(1), pages 415-439, January.
    10. Fadaki, Masih & Abareshi, Ahmad & Far, Shaghayegh Maleki & Lee, Paul Tae-Woo, 2022. "Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    11. Qiu, Huaxin & Wang, Sutong & Yin, Yunqiang & Wang, Dujuan & Wang, Yanzhang, 2022. "A deep reinforcement learning-based approach for the home delivery and installation routing problem," International Journal of Production Economics, Elsevier, vol. 244(C).
    12. Saeed, Naima & Nguyen, Su & Cullinane, Kevin & Gekara, Victor & Chhetri, Prem, 2023. "Forecasting container freight rates using the Prophet forecasting method," Transport Policy, Elsevier, vol. 133(C), pages 86-107.
    13. Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    14. Douglas J. Thomas, 2005. "Measuring Item Fill-Rate Performance in a Finite Horizon," Manufacturing & Service Operations Management, INFORMS, vol. 7(1), pages 74-80, September.
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