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LIFO Inventory Systems

Author

Listed:
  • Morris A. Cohen

    (University of Pennsylvania)

  • Dov Pekelman

    (Tel Aviv University)

Abstract

In many perishable product inventory systems where the issuing of stock to meet demand is controlled by the consumer, the movement of units through the system obeys a LIFO discipline. In this paper the evolution over time of the LIFO inventory stock age distribution in an environment of stochastic demand is analyzed. The inventory related processes are shown to be completely specified by the random walk ladder height process. This characterization of the LIFO system is then utilized to derive limiting distributions for the stock age distribution when a constant amount is ordered each period. The distribution is found from a Wiener-Hopf integral equation for the ladder height random variable. Transient results for the case of a fixed critical number order policy are also presented. Explicit closed form results are derived for an example in which demand is exponentially distributed. The paper concludes by demonstrating how the results on the steady-state age distribution can be used for devising upper and lower bounds on expected shortages and outdates.

Suggested Citation

  • Morris A. Cohen & Dov Pekelman, 1978. "LIFO Inventory Systems," Management Science, INFORMS, vol. 24(11), pages 1150-1162, July.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:11:p:1150-1162
    DOI: 10.1287/mnsc.24.11.1150
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    Cited by:

    1. 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.
    2. Jian Yang, 2004. "Production Control in the Face of Storable Raw Material, Random Supply, and an Outside Market," Operations Research, INFORMS, vol. 52(2), pages 293-311, April.
    3. Phelan, Carolyn E. & Marazzina, Daniele & Fusai, Gianluca & Germano, Guido, 2018. "Fluctuation identities with continuous monitoring and their application to the pricing of barrier options," European Journal of Operational Research, Elsevier, vol. 271(1), pages 210-223.
    4. Qing Li & Peiwen Yu & Xiaoli Wu, 2016. "Managing Perishable Inventories in Retailing: Replenishment, Clearance Sales, and Segregation," Operations Research, INFORMS, vol. 64(6), pages 1270-1284, December.
    5. Jinzhi Bu & Xiting Gong & Xiuli Chao, 2023. "Asymptotic Optimality of Base-Stock Policies for Perishable Inventory Systems," Management Science, INFORMS, vol. 69(2), pages 846-864, February.
    6. Ding, Jingying & Peng, Zhenkang, 2024. "Heuristics for perishable inventory systems under mixture issuance policies," Omega, Elsevier, vol. 126(C).
    7. Liming Liu & Zhaotong Lian, 1999. "(s, S) Continuous Review Models for Products with Fixed Lifetimes," Operations Research, INFORMS, vol. 47(1), pages 150-158, February.
    8. Shouchang Chen & Yanzhi Li & Yi Yang & Weihua Zhou, 2021. "Managing Perishable Inventory Systems with Age‐differentiated Demand," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3784-3799, October.
    9. Li‐Ming Chen & Amar Sapra, 2013. "Joint inventory and pricing decisions for perishable products with two‐period lifetime," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(5), pages 343-366, August.
    10. Carolyn E. Phelan & Daniele Marazzina & Gianluca Fusai & Guido Germano, 2017. "Fluctuation identities with continuous monitoring and their application to price barrier options," Papers 1712.00077, arXiv.org.
    11. Minner, Stefan & Transchel, Sandra, 2017. "Order variability in perishable product supply chains," European Journal of Operational Research, Elsevier, vol. 260(1), pages 93-107.

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