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A queuing approach for inventory planning with batch ordering in multi-echelon supply chains

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  • Sandeep Jain
  • N. Raghavan

Abstract

This paper presents stylized models for conducting performance analysis of the manufacturing supply chain network (SCN) in a stochastic setting for batch ordering. We use queueing models to capture the behavior of SCN. The analysis is clubbed with an inventory optimization model, which can be used for designing inventory policies . In the first case, we model one manufacturer with one warehouse, which supplies to various retailers. We determine the optimal inventory level at the warehouse that minimizes total expected cost of carrying inventory, back order cost associated with serving orders in the backlog queue, and ordering cost. In the second model we impose service level constraint in terms of fill rate (probability an order is filled from stock at warehouse), assuming that customers do not balk from the system. We present several numerical examples to illustrate the model and to illustrate its various features. In the third case, we extend the model to a three-echelon inventory model which explicitly considers the logistics process. Copyright Springer-Verlag 2009

Suggested Citation

  • Sandeep Jain & N. Raghavan, 2009. "A queuing approach for inventory planning with batch ordering in multi-echelon supply chains," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(1), pages 95-110, March.
    • Handle: RePEc:spr:cejnor:v:17:y:2009:i:1:p:95-110
    DOI: 10.1007/s10100-008-0077-8
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    References listed on IDEAS

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    1. Matheus, Peter & Gelders, Ludo, 2000. "The (R, Q) inventory policy subject to a compound Poisson demand pattern," International Journal of Production Economics, Elsevier, vol. 68(3), pages 307-317, December.
    2. Kim, Ilhyung & Tang, Christopher S., 1997. "Lead time and response time in a pull production control system," European Journal of Operational Research, Elsevier, vol. 101(3), pages 474-485, September.
    3. Hong Chen & J. Michael Harrison & Avi Mandelbaum & Ann Van Ackere & Lawrence M. Wein, 1988. "Empirical Evaluation of a Queueing Network Model for Semiconductor Wafer Fabrication," Operations Research, INFORMS, vol. 36(2), pages 202-215, April.
    4. Jing-Sheng Song & David D. Yao, 2002. "Performance Analysis and Optimization of Assemble-to-Order Systems with Random Lead Times," Operations Research, INFORMS, vol. 50(5), pages 889-903, October.
    5. Markus Ettl & Gerald E. Feigin & Grace Y. Lin & David D. Yao, 2000. "A Supply Network Model with Base-Stock Control and Service Requirements," Operations Research, INFORMS, vol. 48(2), pages 216-232, April.
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    Cited by:

    1. C. Y. Lam, 2021. "Optimizing logistics routings in a network perspective of supply and demand nodes," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 357-377, March.
    2. Kevin Geevers & Lotte Hezewijk & Martijn R. K. Mes, 2024. "Multi-echelon inventory optimization using deep reinforcement learning," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(3), pages 653-683, September.
    3. Dai, Zhuo & Aqlan, Faisal & Gao, Kuo, 2017. "Optimizing multi-echelon inventory with three types of demand in supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 141-177.
    4. Shabnam Rekabi & Ali Ghodratnama & Amir Azaron, 2022. "Designing pharmaceutical supply chain networks with perishable items considering congestion," Operational Research, Springer, vol. 22(4), pages 4159-4219, September.
    5. Hoang, Lê Nguyên & Soumis, François & Zaccour, Georges, 2019. "The return function: A new computable perspective on Bayesian–Nash equilibria," European Journal of Operational Research, Elsevier, vol. 279(2), pages 471-485.

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