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A multi-objective hybrid evolutionary approach for buffer allocation in open serial production lines

Author

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  • Simge Yelkenci Kose

    (Turkish Aerospace Industries)

  • Ozcan Kilincci

    (Dokuz Eylul University)

Abstract

The buffer allocation problem is of particular interest for operations management since buffers have a considerable impact on capacity improvement in production systems. In this study, the buffer allocation is solved to optimize two conflicting objectives of maximizing the average system production rate and minimizing total buffer size. A hybrid evolutionary algorithm-based simulation optimization approach is proposed for the multi-objective buffer allocation problem (MOBAP) in open serial production lines. As a search methodology, the Pareto optimal set is derived by hybrid approach using elitist non-dominated sorting genetic algorithm (NSGA-II) and a special version of a multi-objective simulated annealing. As an evaluative tool, discrete event simulation modeling is used to estimate the performance measures for the production systems. To demonstrate the efficacy of the proposed hybrid approach, a comparative study is provided for the MOBAP in various serial line configurations. The comparative results show that the hybrid method has a considerable potential to minimize the total buffer space by appropriately allocating space to each buffer while maximizing average production rate.

Suggested Citation

  • Simge Yelkenci Kose & Ozcan Kilincci, 2020. "A multi-objective hybrid evolutionary approach for buffer allocation in open serial production lines," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 33-51, January.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1435-6
    DOI: 10.1007/s10845-018-1435-6
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    References listed on IDEAS

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    1. Mariano Frutos & Ana Olivera & Fernando Tohmé, 2010. "A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem," Annals of Operations Research, Springer, vol. 181(1), pages 745-765, December.
    2. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    3. Matai, Rajesh, 2015. "Solving multi objective facility layout problem by modified simulated annealing," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 302-311.
    4. Kai Ding & Pingyu Jiang & Mei Zheng, 2017. "Environmental and economic sustainability-aware resource service scheduling for industrial product service systems," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1303-1316, August.
    5. Pourvaziri, Hani & Pierreval, Henri, 2017. "Dynamic facility layout problem based on open queuing network theory," European Journal of Operational Research, Elsevier, vol. 259(2), pages 538-553.
    6. Papadopoulos, H. T. & Heavey, C., 1996. "Queueing theory in manufacturing systems analysis and design: A classification of models for production and transfer lines," European Journal of Operational Research, Elsevier, vol. 92(1), pages 1-27, July.
    7. Dugardin, Frédéric & Yalaoui, Farouk & Amodeo, Lionel, 2010. "New multi-objective method to solve reentrant hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 203(1), pages 22-31, May.
    8. Cruz, F.R.B. & Van Woensel, T. & Smith, J. MacGregor, 2010. "Buffer and throughput trade-offs in M/G/1/K queueing networks: A bi-criteria approach," International Journal of Production Economics, Elsevier, vol. 125(2), pages 224-234, June.
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    Cited by:

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