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Inventory management with partially observed nonstationary demand

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  • Erhan Bayraktar
  • Michael Ludkovski

Abstract

We consider a continuous-time model for inventory management with Markov modulated non-stationary demands. We introduce active learning by assuming that the state of the world is unobserved and must be inferred by the manager. We also assume that demands are observed only when they are completely met. We first derive the explicit filtering equations and pass to an equivalent fully observed impulse control problem in terms of the sufficient statistics, the a posteriori probability process and the current inventory level. We then solve this equivalent formulation and directly characterize an optimal inventory policy. We also describe a computational procedure to calculate the value function and the optimal policy and present two numerical illustrations. Copyright Springer Science+Business Media, LLC 2010

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  • Erhan Bayraktar & Michael Ludkovski, 2010. "Inventory management with partially observed nonstationary demand," Annals of Operations Research, Springer, vol. 176(1), pages 7-39, April.
    • Handle: RePEc:spr:annopr:v:176:y:2010:i:1:p:7-39:10.1007/s10479-009-0513-8
    DOI: 10.1007/s10479-009-0513-8
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    Cited by:

    1. Park, Hyungjun & Choi, Dong Gu & Min, Daiki, 2023. "Adaptive inventory replenishment using structured reinforcement learning by exploiting a policy structure," International Journal of Production Economics, Elsevier, vol. 266(C).
    2. Mike Ludkovski, 2022. "Regression Monte Carlo for Impulse Control," Papers 2203.06539, arXiv.org.
    3. Ricardo Montoya & Carlos Gonzalez, 2019. "A Hidden Markov Model to Detect On-Shelf Out-of-Stocks Using Point-of-Sale Data," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 932-948, October.
    4. Stößlein, Martin & Kanet, John Jack & Gorman, Mike & Minner, Stefan, 2014. "Time-phased safety stocks planning and its financial impacts: Empirical evidence based on European econometric data," International Journal of Production Economics, Elsevier, vol. 149(C), pages 47-55.
    5. Moawia Alghalith, 2012. "A New Stopping Time Model: A Solution to a Free-Boundary Problem," Journal of Optimization Theory and Applications, Springer, vol. 152(1), pages 265-270, January.
    6. Manafzadeh Dizbin, Nima & Tan, Barış, 2020. "Optimal control of production-inventory systems with correlated demand inter-arrival and processing times," International Journal of Production Economics, Elsevier, vol. 228(C).
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    8. Satya S. Malladi & Alan L. Erera & Chelsea C. White, 2023. "Inventory control with modulated demand and a partially observed modulation process," Annals of Operations Research, Springer, vol. 321(1), pages 343-369, February.
    9. Roberto Rossi & S. Tarim & Brahim Hnich & Steven Prestwich, 2012. "Constraint programming for stochastic inventory systems under shortage cost," Annals of Operations Research, Springer, vol. 195(1), pages 49-71, May.
    10. Yee, Hannah & van Staden, Heletjé E. & Boute, Robert N., 2024. "Dual sourcing under non-stationary demand and partial observability," European Journal of Operational Research, Elsevier, vol. 314(1), pages 94-110.
    11. Jianqiang Hu & Cheng Zhang & Chenbo Zhu, 2016. "( s , S ) Inventory Systems with Correlated Demands," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 603-611, November.
    12. Khayyati, Siamak & Tan, Barış, 2020. "Data-driven control of a production system by using marking-dependent threshold policy," International Journal of Production Economics, Elsevier, vol. 226(C).
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