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New MILP models for the permutation flowshop problem

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  • F T Tseng

    (University of Alabama in Huntsville)

  • E F Stafford

    (University of Alabama in Huntsville)

Abstract

Two new mixed-integer linear programming (MILP) models for the regular permutation flowshop problem, called TBA and TS3, are derived using a combination of JAML (job-adjacency, machine-linkage) diagrams and variable substitution techniques. These new models are then compared to the incumbent best MILP models (Wilson, WST2, and TS2) for this problem found in the flowshop sequencing literature. We define the term best to mean that a particular model or set of models can solve a common set of test flowshop problems in significantly less time than other competing models. In other words, the two new MILP models (TBA and TS3) become the challengers to the current incumbent best models (Wilson, WST2, TS2.). Both new models are shown to require less time, on average, than the current best models for solving this set of problems; and the TS3 model is shown to solve these problems in statistically significantly less time than the other four models combined.

Suggested Citation

  • F T Tseng & E F Stafford, 2008. "New MILP models for the permutation flowshop problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(10), pages 1373-1386, October.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:10:d:10.1057_palgrave.jors.2602455
    DOI: 10.1057/palgrave.jors.2602455
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    References listed on IDEAS

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    1. Stafford, Edward F. & Tseng, Fan T., 2002. "Two models for a family of flowshop sequencing problems," European Journal of Operational Research, Elsevier, vol. 142(2), pages 282-293, October.
    2. Richard A. Dudek & Ottis Foy Teuton, 1964. "Development of M -Stage Decision Rule for Scheduling N Jobs Through M Machines," Operations Research, INFORMS, vol. 12(3), pages 471-497, June.
    3. E F Stafford & F T Tseng & J N D Gupta, 2005. "Comparative evaluation of MILP flowshop models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(1), pages 88-101, January.
    4. Alan S. Manne, 1960. "On the Job-Shop Scheduling Problem," Operations Research, INFORMS, vol. 8(2), pages 219-223, April.
    5. Gupta, Jatinder N.D. & Stafford, Edward Jr., 2006. "Flowshop scheduling research after five decades," European Journal of Operational Research, Elsevier, vol. 169(3), pages 699-711, March.
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    Cited by:

    1. Tamás Hajba & Zoltán Horváth, 2015. "MILP models for the optimization of real production lines," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(4), pages 899-912, December.
    2. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.
    3. Mario Levorato & David Sotelo & Rosa Figueiredo & Yuri Frota, 2024. "Efficient solutions to the m-machine robust flow shop under budgeted uncertainty," Annals of Operations Research, Springer, vol. 338(1), pages 765-799, July.
    4. Roshanaei, Vahid & Naderi, Bahman, 2021. "Solving integrated operating room planning and scheduling: Logic-based Benders decomposition versus Branch-Price-and-Cut," European Journal of Operational Research, Elsevier, vol. 293(1), pages 65-78.

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