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A Genetic Approach to Green Flexible Job Shop Problem Under Uncertainty

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Bioinspired Systems for Translational Applications: From Robotics to Social Engineering (IWINAC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14675))

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Abstract

Flexible job shop scheduling problem with energy and makespan minimization objectives, and uncertain processing times that are modeled with intervals is addressed in this work. The problem is solved by a genetic algorithm using a lexicographic goal programming approach and the results evaluated with respect to the lower and upper bounds that come from various sources and methods.

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Acknowledgements

This research has been supported by the Spanish Government under research grants TED2021-131938B-I00 and PID2022-141746OB-I00.

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Authors and Affiliations

  1. Department of Computing, University of Oviedo, Campus of Gijón, Spain

    Sezin Afsar, Jorge Puente, Juan José Palacios & Camino R. Vela

  2. Department of Mathematics, Statistics and Computation, University of Cantabria, Santander, Spain

    Inés González-Rodríguez

Authors
  1. Sezin Afsar
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  2. Jorge Puente
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  3. Juan José Palacios
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  4. Inés González-Rodríguez
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  5. Camino R. Vela
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Corresponding author

Correspondence to Juan José Palacios .

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Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Polytechnic University of Valencia, Valencia, Spain

    Mikel Val Calvo

  3. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Afsar, S., Puente, J., Palacios, J.J., González-Rodríguez, I., Vela, C.R. (2024). A Genetic Approach to Green Flexible Job Shop Problem Under Uncertainty. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_18

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  • DOI: https://doi.org/10.1007/978-3-031-61137-7_18

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  • Online ISBN: 978-3-031-61137-7

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