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Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming

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  • Lee, Sang M.
  • Asllani, Arben A.

Abstract

Flexibility, speed, and efficiency are major challenges for operations managers in today's knowledge-intensive organizations. Such requirements are converted into three production scheduling criteria: (a) minimize the impact of setup times in flexible production lines when moving from one product to another, (b) minimize number of tardy jobs, and (c) minimize overall production time, or makespan, for a given set of products or services. There is a wide range of solution methodologies for such NP-hard scheduling problems. While mathematical programming models provide optimal solutions, they become too complex to model for large scheduling problems. Simultaneously, heuristic approaches are simpler and very often independent of the problem size, but provide "good" rather than optimal solutions. This paper proposes and compares two alternative solutions: 0-1 mixed integer linear programming and genetic programming. It also provides guidelines that can be used by practitioners in the process of selecting the appropriate scheduling methodology.

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  • Lee, Sang M. & Asllani, Arben A., 2004. "Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming," Omega, Elsevier, vol. 32(2), pages 145-153, April.
    • Handle: RePEc:eee:jomega:v:32:y:2004:i:2:p:145-153
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    Cited by:

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    2. Martin, Clarence H, 2009. "A hybrid genetic algorithm/mathematical programming approach to the multi-family flowshop scheduling problem with lot streaming," Omega, Elsevier, vol. 37(1), pages 126-137, February.
    3. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    4. Melissa R. Bowers & Anurag Agarwal, 2007. "Fewer Equipment Changeovers for the Embroidery Process at Oxford Industries," Interfaces, INFORMS, vol. 37(6), pages 526-538, December.
    5. Chung, Ji-Won & Oh, Seog-Moon & Choi, In-Chan, 2009. "A hybrid genetic algorithm for train sequencing in the Korean railway," Omega, Elsevier, vol. 37(3), pages 555-565, June.

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