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Parallel hybrid heuristics for the permutation flow shop problem

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  • Martín Ravetti
  • Carlos Riveros
  • Alexandre Mendes
  • Mauricio Resende
  • Panos Pardalos

Abstract

This paper addresses the Permutation Flowshop Problem with minimization of makespan, which is denoted by Fm|prmu|C max . In the permutational scenario, the sequence of jobs has to remain the same in all machines. The Flowshop Problem (FSP) is known to be NP-hard when more than three machines are considered. Thus, for medium and large scale instances, high-quality heuristics are needed to find good solutions in reasonable time. We propose and analyse parallel hybrid search methods that fully use the computational power of current multi-core machines. The parallel methods combine a memetic algorithm (MA) and several iterated greedy algorithms (IG) running concurrently. Two test scenarios were included, with short and long CPU times. The tests were conducted on the set of benchmark instances introduced by Taillard (Eur. J. Oper. Res. 64:278–285, 1993 ), commonly used to assess the performance of new methods. Results indicate that the use of the MA to manage a pool of solutions is highly effective, allowing the improvement of the best known upper bound for one of the instances. Copyright Springer Science & Business Media, LLC 2012

Suggested Citation

  • Martín Ravetti & Carlos Riveros & Alexandre Mendes & Mauricio Resende & Panos Pardalos, 2012. "Parallel hybrid heuristics for the permutation flow shop problem," Annals of Operations Research, Springer, vol. 199(1), pages 269-284, October.
    • Handle: RePEc:spr:annopr:v:199:y:2012:i:1:p:269-284:10.1007/s10479-011-1056-3
    DOI: 10.1007/s10479-011-1056-3
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    References listed on IDEAS

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    1. Taillard, E., 1990. "Some efficient heuristic methods for the flow shop sequencing problem," European Journal of Operational Research, Elsevier, vol. 47(1), pages 65-74, July.
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    7. Ruiz, Ruben & Maroto, Concepcion, 2005. "A comprehensive review and evaluation of permutation flowshop heuristics," European Journal of Operational Research, Elsevier, vol. 165(2), pages 479-494, September.
    8. Agarwal, Anurag & Colak, Selcuk & Eryarsoy, Enes, 2006. "Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach," European Journal of Operational Research, Elsevier, vol. 169(3), pages 801-815, March.
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    Cited by:

    1. Taha Keshavarz & Nasser Salmasi & Mohsen Varmazyar, 2015. "Minimizing total completion time in the flexible flowshop sequence-dependent group scheduling problem," Annals of Operations Research, Springer, vol. 226(1), pages 351-377, March.
    2. Jan Gmys, 2022. "Exactly Solving Hard Permutation Flowshop Scheduling Problems on Peta-Scale GPU-Accelerated Supercomputers," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2502-2522, September.
    3. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.

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