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Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm

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Optimization and Learning (OLA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1824))

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Abstract

In this work we focus on optimizing a multi-objective formulation of the Job Shop Scheduling Problem (JSP) which considers the minimization of energy consumption as one of the objectives. In practice, users experts in the problem domain but with a low knowledge in metaheuristics usually take an existing algorithm with default settings to optimize problem instances but, in this context, the use of automatic parameter configuration techniques can help to find ad-hoc configurations of algorithms that effectively solve optimization problems. Our aim is to study what improvement in results can be obtained by applying an autoconfiguration approach versus using a set of well-known multi-objective evolutionary algorithms (NSGA-II, SPEA2, SMS-EMOA and MOEA/D) for different instances of the JSP, with varying dimensionality. Our experiments showcase the potential of automated algorithmic configuration for energy-efficient production scheduling, producing better balanced solutions than the multi-objective solvers considered in the study.

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Acknowledgements

This work has been partially funded by the Spanish Ministry of Science and Innovation (grant PID2020-112540RB-C41, AEI/FEDER, UE) and the Basque Government (IT1456-22).

Author information

Authors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, 29071, Málaga, Spain

    Jesús Para & Antonio J. Nebro

  2. ITIS Software, University of Málaga, 29071, Málaga, Spain

    Antonio J. Nebro

  3. TECNALIA, 48160, Derio, Spain

    Javier Del Ser

  4. University of the Basque Country (UPV/EHU), 48013, Bilbao, Spain

    Javier Del Ser

Authors
  1. Jesús Para
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  2. Javier Del Ser
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  3. Antonio J. Nebro
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Corresponding author

Correspondence to Antonio J. Nebro .

Editor information

Editors and Affiliations

  1. University of Cadiz, Cadiz, Spain

    Bernabé Dorronsoro

  2. University of Malaga, Malaga, Spain

    Francisco Chicano

  3. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Gregoire Danoy

  4. University of Lille, Lille, France

    El-Ghazali Talbi

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Para, J., Del Ser, J., Nebro, A.J. (2023). Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm. In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science, vol 1824. Springer, Cham. https://doi.org/10.1007/978-3-031-34020-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-34020-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34019-2

  • Online ISBN: 978-3-031-34020-8

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