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The buffer allocation problem in production lines: Formulations, solution methods, and instances

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  • Sophie Weiss
  • Justus Arne Schwarz
  • Raik Stolletz

Abstract

Flow production lines with finite buffer capacities are used in practice for mass production, e.g., in the automotive and food industries. The decision regarding the allocation of buffer capacities to mitigate throughput losses from stochastic processing times and unreliable stations is known as the Buffer Allocation Problem (BAP). This article classifies and reviews the literature on the BAP with respect to different versions of the optimization problem. It considers the detailed characteristics of the flow lines, the objective function, and the constraints. Moreover, a new classification scheme for solution methods is presented that differentiates between explicit solutions, integrated optimization methods, and iterative optimization methods. The characteristics of test instances derived from realistic cases and test instances used in multiple references are discussed. The review reveals gaps in the literature regarding the considered optimization problems and solution methods, especially with a view on realistic lines. In addition, a library, FlowLineLib, of realistic and already used test instances is provided.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:uiiexx:v:51:y:2019:i:5:p:456-485
    DOI: 10.1080/24725854.2018.1442031
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    Cited by:

    1. 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.
    2. Romero-Silva, Rodrigo & Shaaban, Sabry & Marsillac, Erika & Laarraf, Zouhair, 2021. "The impact of unequal processing time variability on reliable and unreliable merging line performance," International Journal of Production Economics, Elsevier, vol. 235(C).
    3. Bengisu Urlu & Nesim K. Erkip, 2020. "Safety stock placement for serial systems under supply process uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 395-424, June.
    4. Ziwei Lin & Nicla Frigerio & Andrea Matta & Shichang Du, 2021. "Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 223-253, March.

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