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A unified framework for pull control mechanisms in multi‐stage manufacturing systems

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  • George Liberopoulos
  • Yves Dallery

Abstract

This paper presents a unified framework for pull production control mechanisms in multi‐stage manufacturing systems. A pull production control mechanism in a multi‐stage manufacturing system is a mechanism that coordinates the release of parts into each stage of the system with the arrival of customer demands for final products. Four basic pull production control mechanisms are presented: Base Stock, Kanban, Generalized Kanban, and Extended Kanban. It is argued that on top of any of these basic coordination mechanisms, a local mechanism to control the work‐in‐process in each stage may be superimposed. Several cases of basic stage coordination mechanisms with stage work‐in‐process control are presented, and several production control systems that have appeared in the literature are shown to be equivalent to some of these cases. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • George Liberopoulos & Yves Dallery, 2000. "A unified framework for pull control mechanisms in multi‐stage manufacturing systems," Annals of Operations Research, Springer, vol. 93(1), pages 325-355, January.
    • Handle: RePEc:spr:annopr:v:93:y:2000:i:1:p:325-355:10.1023/a:1018980024795
    DOI: 10.1023/A:1018980024795
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    Citations

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    Cited by:

    1. Fernandes, Nuno Octavio & do Carmo-Silva, Silvio, 2006. "Generic POLCA--A production and materials flow control mechanism for quick response manufacturing," International Journal of Production Economics, Elsevier, vol. 104(1), pages 74-84, November.
    2. Kumar Satyam & Ananth Krishnamurthy, 2013. "Performance analysis of CONWIP systems with batch size constraints," Annals of Operations Research, Springer, vol. 209(1), pages 85-114, October.
    3. Rhee, Young, 2006. "Some notes on the reduction of network dimensionality in nested open queueing networks," European Journal of Operational Research, Elsevier, vol. 174(1), pages 124-131, October.
    4. Cigdem Gurgur, 2013. "Optimal configuration of a decentralized, market-driven production/inventory system," Annals of Operations Research, Springer, vol. 209(1), pages 139-157, October.
    5. 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).
    6. Matta, Andrea & Dallery, Yves & Di Mascolo, Maria, 2005. "Analysis of assembly systems controlled with kanbans," European Journal of Operational Research, Elsevier, vol. 166(2), pages 310-336, October.
    7. George Liberopoulos, 2020. "Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 297-365, June.
    8. Wai Kin (Victor) Chan & Lee Schruben, 2008. "Optimization Models of Discrete-Event System Dynamics," Operations Research, INFORMS, vol. 56(5), pages 1218-1237, October.
    9. Matthias Thürer & Nuno O. Fernandes & Mark Stevenson & Cristovao Silva & Silvio Carmo-Silva, 2019. "POLC-A: an assessment of POLCA’s authorization element," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2435-2447, August.
    10. 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.

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