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Simulation optimization of buffer allocations in production lines with unreliable machines

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  • Gül Gürkan

Abstract

We use a recent simulation‐based optimization method, sample path optimization, to find optimal buffer allocations in tandem production lines where machines are subject to random breakdowns and repairs, and the product is fluid‐type. We explore some of the functional properties of throughput of such systems and exploit these properties to prove the almost sure convergence of our optimization technique, under a regularity condition on the steady state. Utilizing a generalized semi‐Markov process (GSMP) representation of the system, we derive recursive expressions to compute one‐sided directional derivatives of throughput, from a single simulation run. Finally, we give computational results for lines with up to 50 machines. We also compare results for smaller lines with the results from a more conventional method, stochastic approximation, whenever applicable. In these numerical studies, our method performed quite well on problems that are considered difficult by current computational standards. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Gül Gürkan, 2000. "Simulation optimization of buffer allocations in production lines with unreliable machines," Annals of Operations Research, Springer, vol. 93(1), pages 177-216, January.
  • Handle: RePEc:spr:annopr:v:93:y:2000:i:1:p:177-216:10.1023/a:1018900729338
    DOI: 10.1023/A:1018900729338
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    Cited by:

    1. Kao, Chiang & Chen, Shih-Pin, 2006. "A stochastic quasi-Newton method for simulation response optimization," European Journal of Operational Research, Elsevier, vol. 173(1), pages 30-46, August.
    2. Zhang, Ning & Qi, Faqun & Zhang, Chengjie & Zhou, Hongming, 2022. "Joint optimization of condition-based maintenance policy and buffer capacity for a two-unit series system," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Dengiz, Berna & İç, Yusuf Tansel & Belgin, Onder, 2016. "A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 120(C), pages 120-128.

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