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An Improved Local Search Genetic Algorithm with Multi-crossover for Job Shop Scheduling Problem

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Abstract

Recent works are using meta-heuristics to address the problem class known in the literature as Job Shop Scheduling Problem (JSSP) because of its complexity, since it consists of combinatorial problems and belongs to the set of NP-Hard computational problems. In this type of problem, one of the most discussed goals is to minimize the makespan, which is the maximum production time of a series of jobs. A widely used meta-heuristic in JSSP is the Genetic Algorithm (GA) due to its good performance in scheduling problems. However, for problems with high complexity, some form of hybridization in GA may be required to improve search space performance, for example, by including specialized local search techniques. It is proposed in this work the use of specialized and improved local search operators in the meta-heuristic GA with multi-crossover strategies in order to minimize makespan in JSSP: The Multi-Crossover Local Search Genetic Algorithm (mXLSGA). Specifically, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. The developed method has been evaluated on 58 instances from well-established benchmarks. Experimental results have proven that mXLSGA is competitive and versatile compared to the state-of-the-art methods.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Authors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Monique Simplicio Viana & Orides Morandin Junior

  2. University of São Paulo, São Carlos, Brazil

    Rodrigo Colnago Contreras

Authors
  1. Monique Simplicio Viana
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  2. Orides Morandin Junior
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  3. Rodrigo Colnago Contreras
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Corresponding author

Correspondence to Monique Simplicio Viana .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Viana, M.S., Junior, O.M., Contreras, R.C. (2020). An Improved Local Search Genetic Algorithm with Multi-crossover for Job Shop Scheduling Problem. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_43

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  • DOI: https://doi.org/10.1007/978-3-030-61401-0_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61400-3

  • Online ISBN: 978-3-030-61401-0

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