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Artificial Intelligence Techniques Applied to the Flowshop and Jobshop Problems. A Review of Recent Literature

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Industry 4.0: The Power of Data

Part of the book series: Lecture Notes in Management and Industrial Engineering ((LNMIE))

Abstract

This paper makes a review of the different contributions that the artificial scheduling in flowshops and jobshops using approaches or techniques from the discipline of artificial intelligence. High quality and growing production is maintained for a promising line of research. The work carries out a literature review following a contrasted methodology that has allowed us to analyze in detail 17 articles published in 2020 and 2021 as a continuation of a previous review. The most outstanding conclusions allow us to see a tendency to improve the use of deep learning techniques in the field of sequencing, both to generate sequences directly and to complement parts of other algorithms.

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References

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Acknowledgements

This research is being funded by project entitled NIOTOME (Ref. RTI2018-102020-B-I00) (MCI/AEI/FEDER, UE). The author María Ángeles Rodríguez was supported by the Generalitat Valenciana (Conselleria de Edu-cación, Investigación, Cultura y Deporte) under Grant-Agreement ACIF/2019/021.

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Authors and Affiliations

  1. Centro de Investigación de Gestión e Ingeniería de Producción (CIGIP), Universitat Politècnica de Valencia, Cno/ de Vera S/N, 46022, Valencia, Spain

    Pedro Gomez-Gasquet, Ana Esteso & Maria Angeles Rodriguez

  2. Departamento de Organización de Empresas, Universitat Politècnica de Valencia, Cno/ de Vera S/N, 46022, Valencia, Spain

    Alejandro Torres

Authors
  1. Pedro Gomez-Gasquet
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  2. Alejandro Torres
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  3. Ana Esteso
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  4. Maria Angeles Rodriguez
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Corresponding author

Correspondence to Pedro Gomez-Gasquet .

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Editors and Affiliations

  1. Department of Management Engineering, University of Burgos, Burgos, Burgos, Spain

    Luis R. Izquierdo

  2. Department of Management Engineering, University of Burgos, Burgos, Burgos, Spain

    José Ignacio Santos

  3. Department of Management Engineering, University of Burgos, Burgos, Burgos, Spain

    Juan José Lavios

  4. Department of Management Engineering, University of Burgos, Burgos, Burgos, Spain

    Virginia Ahedo

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Gomez-Gasquet, P., Torres, A., Esteso, A., Rodriguez, M.A. (2023). Artificial Intelligence Techniques Applied to the Flowshop and Jobshop Problems. A Review of Recent Literature. In: Izquierdo, L.R., Santos, J.I., Lavios, J.J., Ahedo, V. (eds) Industry 4.0: The Power of Data. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-29382-5_14

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  • DOI: https://doi.org/10.1007/978-3-031-29382-5_14

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  • Print ISBN: 978-3-031-29381-8

  • Online ISBN: 978-3-031-29382-5

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