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Optimal Control of a Two-Station Tandem Production/Inventory System

Author

Listed:
  • Michael H. Veatch

    (Gordon College, Wenham, Massachusetts)

  • Lawrence M. Wein

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

A manufacturing facility consisting of two stations in tandem operates in a make-to-stock mode: After production, items are placed in a finished goods inventory that services an exogenous Poisson demand. Demand that cannot be met from inventory is backordered. Each station is modeled as a queue with controllable production rate and exponential service times. The problem is to control these rates to minimize inventory holding and backordering costs. Optimal controls are computed using dynamic programming and compared with the kanban, basestock and buffer control mechanisms that have been proposed for manufacturing facilities. Conditions are found under which certain simple controls are optimal using stochastic coupling arguments. Insights are gained into when to hold work-in-process and finished goods inventory, comparable to previous studies of production lines in make-to-order and unlimited demand environments.

Suggested Citation

  • Michael H. Veatch & Lawrence M. Wein, 1994. "Optimal Control of a Two-Station Tandem Production/Inventory System," Operations Research, INFORMS, vol. 42(2), pages 337-350, April.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:2:p:337-350
    DOI: 10.1287/opre.42.2.337
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    Citations

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    Cited by:

    1. Boray Huang & Seyed M. R. Iravani, 2005. "Production Control Policies in Supply Chains with Selective-Information Sharing," Operations Research, INFORMS, vol. 53(4), pages 662-674, August.
    2. 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.
    3. Manafzadeh Dizbin, Nima & Tan, Barış, 2020. "Optimal control of production-inventory systems with correlated demand inter-arrival and processing times," International Journal of Production Economics, Elsevier, vol. 228(C).
    4. Jing-Sheng Song & Li Xiao & Hanqin Zhang & Paul Zipkin, 2017. "Optimal Policies for a Dual-Sourcing Inventory Problem with Endogenous Stochastic Lead Times," Operations Research, INFORMS, vol. 65(2), pages 379-395, April.
    5. Xu, Jianjun & Serrano, Alejandro & Lin, Bing, 2017. "Optimal production and rationing policy of two-stage tandem production system," International Journal of Production Economics, Elsevier, vol. 185(C), pages 100-112.
    6. Hossein Abouee-Mehrizi & Opher Baron & Oded Berman, 2014. "Exact Analysis of Capacitated Two-Echelon Inventory Systems with Priorities," Manufacturing & Service Operations Management, INFORMS, vol. 16(4), pages 561-577, October.
    7. Samuel Vercraene & Jean-Philippe Gayon & Fikri Karaesmen, 2018. "Effects of System Parameters on the Optimal Cost and Policy in a Class of Multidimensional Queueing Control Problems," Operations Research, INFORMS, vol. 66(1), pages 150-162, January.
    8. Rodney P. Parker & Roman Kapuscinski, 2004. "Optimal Policies for a Capacitated Two-Echelon Inventory System," Operations Research, INFORMS, vol. 52(5), pages 739-755, October.
    9. Seçil Savaşaneril & Paul M. Griffin & Pınar Keskinocak, 2010. "Dynamic Lead-Time Quotation for an M/M/1 Base-Stock Inventory Queue," Operations Research, INFORMS, vol. 58(2), pages 383-395, April.
    10. D. Leeuwen & R. Núñez Queija, 2017. "Optimal dispatching in a tandem queue," Queueing Systems: Theory and Applications, Springer, vol. 87(3), pages 269-291, December.
    11. Hernan Caceres & Dongchen Yu & Alexander Nikolaev, 2018. "Evaluating shortfall distributions in periodic inventory systems with stochastic endogenous demands and lead-times," Annals of Operations Research, Springer, vol. 271(2), pages 405-427, December.
    12. Opher Baron & Oded Berman & Dmitry Krass & Jianfu Wang, 2014. "Using Strategic Idleness to Improve Customer Service Experience in Service Networks," Operations Research, INFORMS, vol. 62(1), pages 123-140, February.

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