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A simulated annealing approach for buffer allocation in reliable production lines

Author

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  • Diomidis Spinellis
  • Chrissoleon Papadopoulos

Abstract

We describe a simulated annealing approach for solving the buffer allocation problem in reliable production lines. The problem entails the determination of near optimal buffer allocation plans in large production lines with the objective of maximizing their average throughput. The latter is calculated utilizing a decomposition method. The allocation plan is calculated subject to a given amount of total buffer slots in a computationally efficient way. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Diomidis Spinellis & Chrissoleon Papadopoulos, 2000. "A simulated annealing approach for buffer allocation in reliable production lines," Annals of Operations Research, Springer, vol. 93(1), pages 373-384, January.
    • Handle: RePEc:spr:annopr:v:93:y:2000:i:1:p:373-384:10.1023/a:1018984125703
    DOI: 10.1023/A:1018984125703
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    Citations

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    Cited by:

    1. Federico Nuñez-Piña & Joselito Medina-Marin & Juan Carlos Seck-Tuoh-Mora & Norberto Hernandez-Romero & Eva Selene Hernandez-Gress, 2018. "Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    2. Elisa Gebennini & Andrea Grassi & Cesare Fantuzzi, 2015. "The two-machine one-buffer continuous time model with restart policy," Annals of Operations Research, Springer, vol. 231(1), pages 33-64, August.
    3. George Liberopoulos & George Kozanidis & Panagiotis Tsarouhas, 2007. "Performance Evaluation of an Automatic Transfer Line with WIP Scrapping During Long Failures," Manufacturing & Service Operations Management, INFORMS, vol. 9(1), pages 62-83, December.
    4. Juliane Müller & Christine Shoemaker & Robert Piché, 2014. "SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications," Journal of Global Optimization, Springer, vol. 59(4), pages 865-889, August.
    5. Raik Martin Bär & Michael Zeilmann & Christoph Nophut & Joachim Kleinert & Karsten Beyer & Tobias Voigt, 2021. "Simulation of Energy and Media Demand of Beverage Bottling Plants by Automatic Model Generation," Sustainability, MDPI, vol. 13(18), pages 1-22, September.
    6. Nabil Nahas, 2017. "Buffer allocation and preventive maintenance optimization in unreliable production lines," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 85-93, January.
    7. 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.
    8. Eva Lee & Siddhartha Maheshwary & Jacquelyn Mason & William Glisson, 2006. "Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks," Annals of Operations Research, Springer, vol. 148(1), pages 25-53, November.
    9. Elisa Gebennini & Andrea Grassi & Cesare Fantuzzi & Stanley Gershwin & Irvin Schick, 2013. "Discrete time model for two-machine one-buffer transfer lines with restart policy," Annals of Operations Research, Springer, vol. 209(1), pages 41-65, October.
    10. Thiago Cantos Lopes & Celso Gustavo Stall Sikora & Adalberto Sato Michels & Leandro Magatão, 2020. "Mixed-model assembly lines balancing with given buffers and product sequence: model, formulation comparisons, and case study," Annals of Operations Research, Springer, vol. 286(1), pages 475-500, March.

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