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Local search heuristics for single machine scheduling with batch set-up times to minimize total weighted completion time

Author

Listed:
  • H.A.J. Crauwels
  • C.N. Potts
  • L.N. Van Wassenhove

Abstract

Local search heuristics are developed for a problem of scheduling a single machine to minimize the total weighted completion time. The jobs are partitioned into families, and a set-up time is necessary when there is a switch in processing jobs from one family to jobs of another family. Four alternative neighbourhood search methods are developed: multi-start descent, simulated annealing, threshold accepting and tabu search. The performance of these heuristics is evaluated on a large set of test problems, and the results are also compared with those obtained by a genetic algorithm. The best results are obtained with the tabu search method for smaller numbers of families and with the genetic algorithm for larger numbers of families. In combination, these methods generate high quality schedules at relatively modest computational expense. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • H.A.J. Crauwels & C.N. Potts & L.N. Van Wassenhove, 1997. "Local search heuristics for single machine scheduling with batch set-up times to minimize total weighted completion time," Annals of Operations Research, Springer, vol. 70(0), pages 261-279, April.
    • Handle: RePEc:spr:annopr:v:70:y:1997:i:0:p:261-279:10.1023/a:1018978322417
    DOI: 10.1023/A:1018978322417
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    Citations

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    Cited by:

    1. Tomasz Gawroński, 2012. "Optimization of setup times in the furniture industry," Annals of Operations Research, Springer, vol. 201(1), pages 169-182, December.
    2. Bennell, Julia A. & Mesgarpour, Mohammad & Potts, Chris N., 2017. "Dynamic scheduling of aircraft landings," European Journal of Operational Research, Elsevier, vol. 258(1), pages 315-327.
    3. Dunstall, Simon & Wirth, Andrew, 2005. "A comparison of branch-and-bound algorithms for a family scheduling problem with identical parallel machines," European Journal of Operational Research, Elsevier, vol. 167(2), pages 283-296, December.
    4. Steinhofel, K. & Albrecht, A. & Wong, C. K., 1999. "Two simulated annealing-based heuristics for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 118(3), pages 524-548, November.
    5. Liji Shen & Lars Mönch & Udo Buscher, 2013. "An iterative approach for the serial batching problem with parallel machines and job families," Annals of Operations Research, Springer, vol. 206(1), pages 425-448, July.
    6. S Karabatı & C Akkan, 2006. "Minimizing sum of completion times on a single machine with sequence-dependent family setup times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(3), pages 271-280, March.
    7. Potts, Chris N. & Kovalyov, Mikhail Y., 2000. "Scheduling with batching: A review," European Journal of Operational Research, Elsevier, vol. 120(2), pages 228-249, January.
    8. Allahverdi, Ali & Gupta, Jatinder N. D. & Aldowaisan, Tariq, 1999. "A review of scheduling research involving setup considerations," Omega, Elsevier, vol. 27(2), pages 219-239, April.
    9. Kramer, Arthur & Iori, Manuel & Lacomme, Philippe, 2021. "Mathematical formulations for scheduling jobs on identical parallel machines with family setup times and total weighted completion time minimization," European Journal of Operational Research, Elsevier, vol. 289(3), pages 825-840.

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