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Stock Rationing in a Continuous Review Two-Echelon Inventory Model

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  • Sven Axsäter
  • Marcel Kleijn
  • Ton de Kok

Abstract

In this paper we consider a 1-warehouse, N-retailer inventory system where demand occurs at all locations. We introduce an inventory model which allows us to set different service levels for retailers and direct customer demand at the warehouse. For each retailer a critical level is defined, such that a retailer replenishment order is delivered from warehouse stock if and only if the stock level exceeds this critical level. It is assumed that retailer replenishment orders, which are not satisfied from warehouse stock, are delivered directly from the outside supplier, instead of being backlogged. We present an analytical upper bound on the total cost of the system, and develop a heuristic method to optimize the policy parameters. Numerical experiments indicate that our technique provides a very close approximation of the exact cost. Also, we show that differentiating among the retailers and direct customer demand can yield significant cost reductions. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Sven Axsäter & Marcel Kleijn & Ton de Kok, 2004. "Stock Rationing in a Continuous Review Two-Echelon Inventory Model," Annals of Operations Research, Springer, vol. 126(1), pages 177-194, February.
    • Handle: RePEc:spr:annopr:v:126:y:2004:i:1:p:177-194:10.1023/b:anor.0000012280.68155.a3
    DOI: 10.1023/B:ANOR.0000012280.68155.a3
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    Citations

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    Cited by:

    1. de Kok, Ton & Grob, Christopher & Laumanns, Marco & Minner, Stefan & Rambau, Jörg & Schade, Konrad, 2018. "A typology and literature review on stochastic multi-echelon inventory models," European Journal of Operational Research, Elsevier, vol. 269(3), pages 955-983.
    2. Teunter, Ruud H. & Klein Haneveld, Willem K., 2008. "Dynamic inventory rationing strategies for inventory systems with two demand classes, Poisson demand and backordering," European Journal of Operational Research, Elsevier, vol. 190(1), pages 156-178, October.
    3. Tempelmeier, Horst, 2006. "Supply chain inventory optimization with two customer classes in discrete time," European Journal of Operational Research, Elsevier, vol. 174(1), pages 600-621, October.
    4. Boray Huang & Seyed M. R. Iravani, 2008. "Technical Note---A Make-to-Stock System with Multiple Customer Classes and Batch Ordering," Operations Research, INFORMS, vol. 56(5), pages 1312-1320, October.
    5. van Wingerden, E. & Tan, T. & Van Houtum, G.J., 2019. "The impact of an emergency warehouse in a two-echelon spare parts network," European Journal of Operational Research, Elsevier, vol. 276(3), pages 983-997.
    6. A. Saavedra-Nieves & I. García-Jurado & M. G. Fiestras-Janeiro, 2018. "On coalition formation in a non-convex multi-agent inventory problem," Annals of Operations Research, Springer, vol. 261(1), pages 255-273, February.
    7. Erhun Özkan & Geert-Jan Houtum & Yasemin Serin, 2015. "A new approximate evaluation method for two-echelon inventory systems with emergency shipments," Annals of Operations Research, Springer, vol. 224(1), pages 147-169, January.
    8. Bing Lin & Shaoxiang Chen & Yi Feng & Jianjun Xu, 2018. "The Joint Stock and Capacity Rationings of a Make-To-Stock System with Flexible Demand," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(01), pages 1-27, February.
    9. Mohammad Najjartabar Bisheh & G. Reza Nasiri & Esmaeil Esmaeili & Hamid Davoudpour & Shing I. Chang, 2022. "A new supply chain distribution network design for two classes of customers using transfer recurrent neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2604-2618, October.
    10. Hung, Yi-Feng & Hsiao, Jui-Yi, 2013. "Inventory rationing decision models during replenishment lead time," International Journal of Production Economics, Elsevier, vol. 144(1), pages 290-300.

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