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Global Bacteria Optimization Meta-Heuristic Algorithm for Jobshop Scheduling

Author

Listed:
  • Jairo R. Montoya-Torres

    (Universidad de La Sabana, Colombia)

  • Libardo S. Gómez-Vizcaíno

    (Fundación Centro de Investigación en Modelación Empresarial del Caribe, Colombia)

  • Elyn L. Solano-Charris

    (Universidad de La Sabana, Colombia)

  • Carlos D. Paternina-Arboleda

    (Universidad del Norte, Colombia)

Abstract

This paper examines the problem of jobshop scheduling with either makespan minimization or total tardiness minimization, which are both known to be NP-hard. The authors propose the use of a meta-heuristic procedure inspired from bacterial phototaxis. This procedure, called Global Bacteria Optimization (GBO), emulates the reaction of some organisms (bacteria) to light stimulation. Computational experiments are performed using well-known instances from literature. Results show that the algorithm equals and even outperforms previous state-of-the-art procedures in terms of quality of solution and requires very short computational time.

Suggested Citation

  • Jairo R. Montoya-Torres & Libardo S. Gómez-Vizcaíno & Elyn L. Solano-Charris & Carlos D. Paternina-Arboleda, 2010. "Global Bacteria Optimization Meta-Heuristic Algorithm for Jobshop Scheduling," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 1(4), pages 47-58, October.
    • Handle: RePEc:igg:joris0:v:1:y:2010:i:4:p:47-58
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    Cited by:

    1. Luis F. Machado-Domínguez & Carlos D. Paternina-Arboleda & Jorge I. Vélez & Agustín Barrios-Sarmiento, 2022. "An adaptative bacterial foraging optimization algorithm for solving the MRCPSP with discounted cash flows," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 221-248, July.

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