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Multiobjective flow‐shop scheduling

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  • Richard L. Daniels
  • Robert J. Chambers

Abstract

Previous research on the scheduling of multimachine systems has generally focused on the optimization of individual performance measures. This article considers the sequencing of jobs through a multimachine flow shop, where the quality of the resulting schedule is evaluated according to the associated levels of two scheduling criteria, schedule makespan (Cmax) and maximum job tardiness (Tmax). We present constructive procedures that quantify the trade‐off between Cmax and Tmax. The significance of this trade‐off is that the optimal solution for any preference function involving only Cmax and Tmax must be contained among the set of efficient schedules that comprise the trade‐off curve. For the special case of two‐machine flow shops, we present an algorithm that identifies the exact set of efficient schedules. Heruistic procedures for approximating the efficient set are also provided for problems involving many jobs or larger flow shops. Computational results are reported for the procedures which indicate that both the number of efficient schedules and the error incurred by heuristically approximating the efficient set are quite small.

Suggested Citation

  • Richard L. Daniels & Robert J. Chambers, 1990. "Multiobjective flow‐shop scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(6), pages 981-995, December.
    • Handle: RePEc:wly:navres:v:37:y:1990:i:6:p:981-995
    DOI: 10.1002/1520-6750(199012)37:63.0.CO;2-H
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    References listed on IDEAS

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    1. Rosser T. Nelson & Rakesh K. Sarin & Richard L. Daniels, 1986. "Scheduling with Multiple Performance Measures: The One-Machine Case," Management Science, INFORMS, vol. 32(4), pages 464-479, April.
    2. Tapan Sen & Farhad M. E. Raiszadeh & Parthasarati Dileepan, 1988. "Note---A Branch-and-Bound Approach to the Bicriterion Scheduling Problem Involving Total Flowtime and Range of Lateness," Management Science, INFORMS, vol. 34(2), pages 254-260, February.
    3. Van Wassenhove, Luk N. & Baker, Kenneth R., 1982. "A bicriterion approach to time/cost trade-offs in sequencing," European Journal of Operational Research, Elsevier, vol. 11(1), pages 48-54, September.
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    Cited by:

    1. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a combined model based on multi-objective optimization for electrical load forecasting," Energy, Elsevier, vol. 119(C), pages 1057-1074.
    2. Vincent T’kindt & Karima Bouibede-Hocine & Carl Esswein, 2007. "Counting and enumeration complexity with application to multicriteria scheduling," Annals of Operations Research, Springer, vol. 153(1), pages 215-234, September.
    3. Yenisey, Mehmet Mutlu & Yagmahan, Betul, 2014. "Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends," Omega, Elsevier, vol. 45(C), pages 119-135.
    4. Varadharajan, T.K. & Rajendran, Chandrasekharan, 2005. "A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs," European Journal of Operational Research, Elsevier, vol. 167(3), pages 772-795, December.
    5. Yepes-Borrero, Juan C. & Perea, Federico & Ruiz, Rubén & Villa, Fulgencia, 2021. "Bi-objective parallel machine scheduling with additional resources during setups," European Journal of Operational Research, Elsevier, vol. 292(2), pages 443-455.
    6. Mohamed Anis Allouche, 2010. "Manager’s Preferences Modeling within Multi-Criteria Flowshop Scheduling Problem: A Metaheuristic Approach," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 1(2), pages 33-45, December.

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