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A Genetic Algorithm to Minimize the Total Tardiness for M-Machine Permutation Flowshop Problems

Author

Listed:
  • Chia-Shin Chung

    (Department of Operations and Supply Chain Management, Cleveland State Univesity)

  • James Flynn

    (Department of Operations and Supply Chain Management, Cleveland State Univesity)

  • Walter Rom

    (Department of Operations and Supply Chain Management, Cleveland State Univesity)

  • Piotr Staliński

    (Department of Quantitative Methods in Management, Wyższa Szkoła Biznesu-National Louis University)

Abstract

The m-machine, n-job, permutation flowshop problem with the total tardiness objective is a common scheduling problem, known to be NP-hard. Branch and bound, the usual approach to finding an optimal solution, experiences difficulty when n exceeds 20. Here, we develop a genetic algorithm, GA, which can handle problems with larger n. We also undertake a numerical study comparing GA with an optimal branch and bound algorithm, and various heuristic algorithms including the well known NEH algorithm and a local search heuristic LH. Extensive computational experiments indicate that LH is an effective heuristic and GA can produce noticeable improvements over LH.

Suggested Citation

  • Chia-Shin Chung & James Flynn & Walter Rom & Piotr Staliński, 2012. "A Genetic Algorithm to Minimize the Total Tardiness for M-Machine Permutation Flowshop Problems," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 8(2), pages 26-43.
  • Handle: RePEc:aae:journl:v:8:y:2012:i:2:p:26-43
    DOI: 10.7341/2012822
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    References listed on IDEAS

    as
    1. Chung, Chia-Shin & Flynn, James & Kirca, Omer, 2006. "A branch and bound algorithm to minimize the total tardiness for m-machine permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 174(1), pages 1-10, October.
    2. Colin R. Reeves, 1997. "Feature Article---Genetic Algorithms for the Operations Researcher," INFORMS Journal on Computing, INFORMS, vol. 9(3), pages 231-250, August.
    3. Chung, Chia-Shin & Flynn, James & Kirca, Omer, 2002. "A branch and bound algorithm to minimize the total flow time for m-machine permutation flowshop problems," International Journal of Production Economics, Elsevier, vol. 79(3), pages 185-196, October.
    4. 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.
    5. Kim, Yeong-Dae, 1995. "Minimizing total tardiness in permutation flowshops," European Journal of Operational Research, Elsevier, vol. 85(3), pages 541-555, September.
    6. 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.
    7. Christos Koulamas, 1994. "The Total Tardiness Problem: Review and Extensions," Operations Research, INFORMS, vol. 42(6), pages 1025-1041, December.
    8. O Etiler & B Toklu & M Atak & J Wilson, 2004. "A genetic algorithm for flow shop scheduling problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 830-835, August.
    9. Arroyo, Jose Elias Claudio & Armentano, Vinicius Amaral, 2005. "Genetic local search for multi-objective flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 167(3), pages 717-738, December.
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