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Optimal inventory decisions in a multiperiod newsvendor problem with partially observed Markovian supply capacities

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  • Wang, Haifeng
  • Chen, Bocheng
  • Yan, Houmin

Abstract

This paper considers a multi-period news-vendor problem with partially observed supply-capacity information which evolves as a Markovian Process. The supply capacity is fully observed by the buyer when the capacity is smaller than the buyer's ordering quantity. Otherwise, the buyer knows that the current-period supply capacity is greater than its ordering quantity. Based on these two observations, the buyer updates the future supply-capacity forecasting accordingly. With a dynamic programming formulation, we prove the existence of an optimal ordering policy. We also prove that the optimal order quantity is greater than the myopic order quantity.

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  • Wang, Haifeng & Chen, Bocheng & Yan, Houmin, 2010. "Optimal inventory decisions in a multiperiod newsvendor problem with partially observed Markovian supply capacities," European Journal of Operational Research, Elsevier, vol. 202(2), pages 502-517, April.
  • Handle: RePEc:eee:ejores:v:202:y:2010:i:2:p:502-517
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    Cited by:

    1. Øksendal, Bernt & Sandal, Leif & Ubøe, Jan, 2013. "Stochastic Stackelberg equilibria with applications to time-dependent newsvendor models," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages 1284-1299.
    2. S. Alireza Yavari & S. M. T. Fatemi Ghomi & Fariborz Jolai, 2024. "Development of multi-period, multi-product model to mitigate supply risk with capacity constraint and discount," OPSEARCH, Springer;Operational Research Society of India, vol. 61(4), pages 2285-2311, December.
    3. Qiu, Ruozhen & Sun, Minghe & Lim, Yun Fong, 2017. "Optimizing (s, S) policies for multi-period inventory models with demand distribution uncertainty: Robust dynamic programing approaches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 880-892.
    4. Jingtang Ma & Shan Yang, 2024. "High-dimensional stochastic control models for newsvendor problems and deep learning resolution," Annals of Operations Research, Springer, vol. 339(1), pages 789-811, August.

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