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A hybrid single and dual population search procedure for the job shop scheduling problem

Author

Listed:
  • V. SELS

  • K. CRAEYMEERSCH
  • M. VANHOUCKE

Abstract

This paper presents a genetic algorithm and a scatter search procedure to solve the well-known job shop scheduling problem. In contrast to the single population search performed by the genetic algorithm, the scatter search algorithm splits the population of solutions in a diverse and high-quality set to exchange information between individuals in a controlled way. Extensions from a single to a dual population by taking problem specific characteristics into account can be seen as a stimulator to add diversity in the search process, which has a positive influence on the important balance between intensification and diversification. Computational experiments verify the benefit of this diversity on the effectiveness of the meta-heuristic search process. Various algorithmic parameters from literature are embedded in both procedures and a detailed comparison is made. A set of standard instances is used to compare the different approaches and the best obtained results are benchmarked against heuristic solutions found in literature.

Suggested Citation

  • V. Sels & K. Craeymeersch & M. Vanhoucke, 2010. "A hybrid single and dual population search procedure for the job shop scheduling problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/679, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:10/679
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    Cited by:

    1. Zhang, Rui & Song, Shiji & Wu, Cheng, 2013. "A hybrid artificial bee colony algorithm for the job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 167-178.
    2. Zhang, Rui & Chang, Pei-Chann & Wu, Cheng, 2013. "A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production," International Journal of Production Economics, Elsevier, vol. 145(1), pages 38-52.
    3. González, Miguel A. & Vela, Camino R. & Varela, Ramiro, 2015. "Scatter search with path relinking for the flexible job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 245(1), pages 35-45.
    4. Chong Peng & Guanglin Wu & T Warren Liao & Hedong Wang, 2019. "Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-19, September.
    5. Habibeh Nazif, 2015. "Solving Job Shop Scheduling Problem Using an Ant Colony Algorithm," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 5(5), pages 261-268, May.

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