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A comparison of combat genetic and big bang–big crunch algorithms for solving the buffer allocation problem

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  • Mehmet Ulaş Koyuncuoğlu

    (Pamukkale University)

  • Leyla Demir

    (Izmir Bakircay University)

Abstract

The buffer allocation problem (BAP) aims to determine the optimal buffer configuration for a production line under the predefined constraints. The BAP is an NP-hard combinatorial optimization problem and the solution space exponentially grows as the problem size increases. Therefore, problem specific heuristic or meta-heuristic search algorithms are widely used to solve the BAP. In this study two population-based search algorithms; i.e. Combat Genetic Algorithm (CGA) and Big Bang-Big Crunch (BB-BC) algorithm, are proposed in solving the BAP to maximize the throughput of the line under the total buffer size constraint for unreliable production lines. Performances of the proposed algorithms are tested on existing benchmark problems taken from the literature. The experimental results showed that the proposed BB–BC algorithm yielded better results than the proposed CGA as well as other algorithms reported in the literature.

Suggested Citation

  • Mehmet Ulaş Koyuncuoğlu & Leyla Demir, 2021. "A comparison of combat genetic and big bang–big crunch algorithms for solving the buffer allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1529-1546, August.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-020-01647-1
    DOI: 10.1007/s10845-020-01647-1
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    References listed on IDEAS

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    1. Stanley Gershwin & James Schor, 2000. "Efficient algorithms for buffer space allocation," Annals of Operations Research, Springer, vol. 93(1), pages 117-144, January.
    2. Ernest Koenigsberg, 1959. "Production Lines and Internal Storage--A Review," Management Science, INFORMS, vol. 5(4), pages 410-433, July.
    3. Sophie Weiss & Justus Arne Schwarz & Raik Stolletz, 2019. "The buffer allocation problem in production lines: Formulations, solution methods, and instances," IISE Transactions, Taylor & Francis Journals, vol. 51(5), pages 456-485, May.
    4. Sophie Weiss & Andrea Matta & Raik Stolletz, 2018. "Optimization of buffer allocations in flow lines with limited supply," IISE Transactions, Taylor & Francis Journals, vol. 50(3), pages 191-202, March.
    5. James MacGregor Smith, 2018. "Simultaneous buffer and service rate allocation in open finite queueing networks," IISE Transactions, Taylor & Francis Journals, vol. 50(3), pages 203-216, March.
    6. Thiago Cantos Lopes & Celso Gustavo Stall Sikora & Adalberto Sato Michels & Leandro Magatão, 2020. "An iterative decomposition for asynchronous mixed-model assembly lines: combining balancing, sequencing, and buffer allocation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(2), pages 615-630, January.
    7. Frederick S. Hillier & Kut C. So & Ronald W. Boling, 1993. "Notes: Toward Characterizing the Optimal Allocation of Storage Space in Production Line Systems with Variable Processing Times," Management Science, INFORMS, vol. 39(1), pages 126-133, January.
    8. Stanley B. Gershwin, 1987. "An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking," Operations Research, INFORMS, vol. 35(2), pages 291-305, April.
    9. Rodrigo Romero-Silva & Sabry Shaaban, 2019. "Influence of unbalanced operation time means and uneven buffer allocation on unreliable merging assembly line efficiency," International Journal of Production Research, Taylor & Francis Journals, vol. 57(6), pages 1645-1666, March.
    10. Giulia Pedrielli & Andrea Matta & Arianna Alfieri & Mengyi Zhang, 2018. "Design and control of manufacturing systems: a discrete event optimisation methodology," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 543-564, January.
    11. Nahas, Nabil & Ait-Kadi, Daoud & Nourelfath, Mustapha, 2006. "A new approach for buffer allocation in unreliable production lines," International Journal of Production Economics, Elsevier, vol. 103(2), pages 873-881, October.
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