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Inventory Management with an Exogenous Supply Process

Author

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  • Alp Muharremoglu

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Nan Yang

    (Johnson School of Management, Cornell University, Ithaca, New York 14853)

Abstract

We study single and multistage inventory systems with stochastic lead times. We study a class of stochastic lead time processes, which we refer to as exogenous lead times. This class of lead time processes includes as special cases all lead time models from existing literature (such as Kaplan's lead times with no order crossing or independent and identically distributed lead times with order crossing, among others) but is a substantially broader class. For a system with an exogenous lead time process, we provide a method to determine base-stock levels and to compute the cost of a given base-stock policy. The method relies on relating the cost of a base-stock policy to the cost of a threshold policy in a related single-unit, single-customer problem. This single-unit method is exact for single-stage systems and for multistage systems under certain conditions. If the conditions are not satisfied, the method obtains near-optimal base-stock levels and accurate approximations of cost for multistage systems.

Suggested Citation

  • Alp Muharremoglu & Nan Yang, 2010. "Inventory Management with an Exogenous Supply Process," Operations Research, INFORMS, vol. 58(1), pages 111-129, February.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:1:p:111-129
    DOI: 10.1287/opre.1090.0706
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    References listed on IDEAS

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    3. Ben-Ammar, Oussama & Bettayeb, Belgacem & Dolgui, Alexandre, 2019. "Optimization of multi-period supply planning under stochastic lead times and a dynamic demand," International Journal of Production Economics, Elsevier, vol. 218(C), pages 106-117.
    4. Chatfield, Dean C. & Pritchard, Alan M., 2018. "Crossover aware base stock decisions for service-driven systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 312-330.
    5. Weiwei Li & Lisheng Weng & Kaixu Zhao & Sidong Zhao & Ping Zhang, 2021. "Research on the Evaluation of Real Estate Inventory Management in China," Land, MDPI, vol. 10(12), pages 1-29, November.
    6. Song, Dong-Ping & Dong, Jing-Xin & Xu, Jingjing, 2014. "Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties," European Journal of Operational Research, Elsevier, vol. 232(3), pages 522-536.
    7. Disney, Stephen M. & Maltz, Arnold & Wang, Xun & Warburton, Roger D.H., 2016. "Inventory management for stochastic lead times with order crossovers," European Journal of Operational Research, Elsevier, vol. 248(2), pages 473-486.
    8. Hellemans, Tim & Boute, Robert N. & Van Houdt, Benny, 2019. "Analysis of lead time correlation under a base-stock policy," European Journal of Operational Research, Elsevier, vol. 276(2), pages 519-535.
    9. Kumar Muthuraman & Sridhar Seshadri & Qi Wu, 2015. "Inventory Management with Stochastic Lead Times," Mathematics of Operations Research, INFORMS, vol. 40(2), pages 302-327, February.
    10. Li Chen & Jing-Sheng Song & Yue Zhang, 2017. "Serial Inventory Systems with Markov-Modulated Demand: Derivative Bounds, Asymptotic Analysis, and Insights," Operations Research, INFORMS, vol. 65(5), pages 1231-1249, October.
    11. Warsing, Donald P. & Wangwatcharakul, Worawut & King, Russell E., 2019. "Computing base-stock levels for a two-stage supply chain with uncertain supply," Omega, Elsevier, vol. 89(C), pages 92-109.
    12. Marcus Ang & Karl Sigman & Jing-Sheng Song & Hanqin Zhang, 2017. "Closed-Form Approximations for Optimal ( r , q ) and ( S , T ) Policies in a Parallel Processing Environment," Operations Research, INFORMS, vol. 65(5), pages 1414-1428, October.

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