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Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach

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  • F Sivrikaya şerifoğlu

    (Abant Izzet Baysal University)

  • G Ulusoy

    (Sabanci University)

Abstract

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-hfsp .

Suggested Citation

  • F Sivrikaya şerifoğlu & G Ulusoy, 2004. "Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(5), pages 504-512, May.
    • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:5:d:10.1057_palgrave.jors.2601716
    DOI: 10.1057/palgrave.jors.2601716
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    References listed on IDEAS

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    5. Oguz, C. & Fikret Ercan, M. & Edwin Cheng, T. C. & Fung, Y. F., 2003. "Heuristic algorithms for multiprocessor task scheduling in a two-stage hybrid flow-shop," European Journal of Operational Research, Elsevier, vol. 149(2), pages 390-403, September.
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    Cited by:

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    3. K-C Ying, 2009. "An iterated greedy heuristic for multistage hybrid flowshop scheduling problems with multiprocessor tasks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 810-817, June.
    4. Carlos Paternina-Arboleda & Jairo Montoya-Torres & Milton Acero-Dominguez & Maria Herrera-Hernandez, 2008. "Scheduling jobs on a k-stage flexible flow-shop," Annals of Operations Research, Springer, vol. 164(1), pages 29-40, November.
    5. Cha, Young-Ho & Kim, Yeong-Dae, 2010. "Fire scheduling for planned artillery attack operations under time-dependent destruction probabilities," Omega, Elsevier, vol. 38(5), pages 383-392, October.
    6. Chou, Fuh-Der, 2013. "Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks," International Journal of Production Economics, Elsevier, vol. 141(1), pages 137-145.
    7. Zhen Song & Håkan Schunnesson & Mikael Rinne & John Sturgul, 2015. "Intelligent Scheduling for Underground Mobile Mining Equipment," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.

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