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Optimal Policies for Production/Inventory Systems with Finite Capacity and Markov-Modulated Demand and Supply Processes

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  • Guillermo Gallego
  • Haichao Hu

Abstract

In many production/inventory systems, not only is the production/inventory capacity finite, but the systems are also subject to random production yields that are influenced by factors such as breakdowns, repairs, maintenance, learning, and the introduction of new technologies. In this paper, we consider a single-item, single-location, periodic-review model with finite capacity and Markov modulated demand and supply processes. When demand and supply processes are driven by two independent, discrete-time, finite-state, time-homogeneous Markov chains, we show that a modified, state-dependent, inflated base-stock policy is optimal for both the finite and infinite horizon planning problems. We also show that the finite-horizon solution converges to the infinite-horizon solution. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Guillermo Gallego & Haichao Hu, 2004. "Optimal Policies for Production/Inventory Systems with Finite Capacity and Markov-Modulated Demand and Supply Processes," Annals of Operations Research, Springer, vol. 126(1), pages 21-41, February.
    • Handle: RePEc:spr:annopr:v:126:y:2004:i:1:p:21-41:10.1023/b:anor.0000012274.69117.90
    DOI: 10.1023/B:ANOR.0000012274.69117.90
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    Citations

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    Cited by:

    1. Bendre, Abhijit Bhagwan & Nielsen, Lars Relund, 2013. "Inventory control in a lost-sales setting with information about supply lead times," International Journal of Production Economics, Elsevier, vol. 142(2), pages 324-331.
    2. 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.
    3. Ozgun Caliskan-Demirag & Youhua Chen & Yi Yang, 2013. "Production-inventory control policy under warm/cold state-dependent fixed costs and stochastic demand: partial characterization and heuristics," Annals of Operations Research, Springer, vol. 208(1), pages 531-556, September.
    4. Peng, Yang & Yan, Xiaoming & Jiang, Yujie & Ji, Min & Cheng, T.C.E., 2021. "Competition and coordination for supply chain networks with random yields," International Journal of Production Economics, Elsevier, vol. 239(C).
    5. Hekimoğlu, Mustafa & van der Laan, Ervin & Dekker, Rommert, 2018. "Markov-modulated analysis of a spare parts system with random lead times and disruption risks," European Journal of Operational Research, Elsevier, vol. 269(3), pages 909-922.
    6. Arifoglu, Kenan & Özekici, Süleyman, 2011. "Inventory management with random supply and imperfect information: A hidden Markov model," International Journal of Production Economics, Elsevier, vol. 134(1), pages 123-137, November.
    7. Chen, Junlin & Zhao, Xiaobo & Zhou, Yun, 2012. "A periodic-review inventory system with a capacitated backup supplier for mitigating supply disruptions," European Journal of Operational Research, Elsevier, vol. 219(2), pages 312-323.
    8. Kutzner, Sarah C. & Kiesmüller, Gudrun P., 2013. "Optimal control of an inventory-production system with state-dependent random yield," European Journal of Operational Research, Elsevier, vol. 227(3), pages 444-452.
    9. Kimitoshi Sato & Kyoko Yagi & Masahito Shimazaki, 2018. "A Stochastic Inventory Model for a Random Yield Supply Chain with Wholesale-Price and Shortage Penalty Contracts," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(06), pages 1-30, December.
    10. Arifoglu, Kenan & Özekici, Süleyman, 2010. "Optimal policies for inventory systems with finite capacity and partially observed Markov-modulated demand and supply processes," European Journal of Operational Research, Elsevier, vol. 204(3), pages 421-438, August.
    11. Sujit De & Shib Sana, 2015. "Backlogging EOQ model for promotional effort and selling price sensitive demand- an intuitionistic fuzzy approach," Annals of Operations Research, Springer, vol. 233(1), pages 57-76, October.
    12. Pirayesh Neghab, Davood & Khayyati, Siamak & Karaesmen, Fikri, 2022. "An integrated data-driven method using deep learning for a newsvendor problem with unobservable features," European Journal of Operational Research, Elsevier, vol. 302(2), pages 482-496.
    13. Canyakmaz, Caner & Özekici, Süleyman & Karaesmen, Fikri, 2019. "An inventory model where customer demand is dependent on a stochastic price process," International Journal of Production Economics, Elsevier, vol. 212(C), pages 139-152.

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