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Clustered enhanced differential evolution for the blocking flow shop scheduling problem

Author

Listed:
  • Donald Davendra
  • Ivan Zelinka
  • Magdalena Bialic-Davendra
  • Roman Senkerik
  • Roman Jasek

Abstract

A novel clustered population paradigm is presented in this paper which is based on Chaos principles of edges and attractors. Convergence in evolutionary algorithms is viewed as a manifestation through cyclic dynamics and thus a new population is developed which is clustered and separated through new segregation bias rules. This population is embedded on the Enhanced Differential Evolution and the flow shop scheduling problem with blocking is solved. The two flow shop benchmark problems of Rec/Car/Hel and Taillard are solved with this new approach and the results favorably compared with published results in literature. A total of 49 new upper bounds for the Taillard problems was obtained during experimentation. Copyright Springer-Verlag 2012

Suggested Citation

  • Donald Davendra & Ivan Zelinka & Magdalena Bialic-Davendra & Roman Senkerik & Roman Jasek, 2012. "Clustered enhanced differential evolution for the blocking flow shop scheduling problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 679-717, December.
    • Handle: RePEc:spr:cejnor:v:20:y:2012:i:4:p:679-717
    DOI: 10.1007/s10100-011-0198-3
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    References listed on IDEAS

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    1. Débora Ronconi, 2005. "A Branch-and-Bound Algorithm to Minimize the Makespan in a Flowshop with Blocking," Annals of Operations Research, Springer, vol. 138(1), pages 53-65, September.
    2. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    3. Onwubolu, Godfrey & Davendra, Donald, 2006. "Scheduling flow shops using differential evolution algorithm," European Journal of Operational Research, Elsevier, vol. 171(2), pages 674-692, June.
    4. J. Heller, 1960. "Some Numerical Experiments for an M × J Flow Shop and its Decision-Theoretical Aspects," Operations Research, INFORMS, vol. 8(2), pages 178-184, April.
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    6. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    7. Grabowski, Jøzef & Pempera, Jaroslaw, 2007. "The permutation flow shop problem with blocking. A tabu search approach," Omega, Elsevier, vol. 35(3), pages 302-311, June.
    8. Caraffa, Vince & Ianes, Stefano & P. Bagchi, Tapan & Sriskandarajah, Chelliah, 2001. "Minimizing makespan in a blocking flowshop using genetic algorithms," International Journal of Production Economics, Elsevier, vol. 70(2), pages 101-115, March.
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    Cited by:

    1. Marcelo Seido Nagano & Adriano Seiko Komesu & Hugo Hissashi Miyata, 2019. "An evolutionary clustering search for the total tardiness blocking flow shop problem," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1843-1857, April.

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