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Machine Learning in Production Scheduling: An Overview of the Academic Literature

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Dynamics in Logistics (LDIC 2020)

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Abstract

Production scheduling is an important tool for a manufacturing system, where it can have a significant impact on the productivity of a production process. In this sense, the application of machine learning can be very fruitful in this field, since it is an enabling computer programs to automatically make intelligent decisions based on data to improve performance at the manufacturing system. Therefore, this paper aims to explore the use of machine learning in production scheduling under the Industry 4.0 context. A systematic literature review was conducted to identify the main machine learning techniques currently employed to improve production scheduling. As a result, bibliometric analysis evidenced the continuous growth of this research area and identified the main machine learning techniques applied. Finally, the gaps leading to further research are highlighted.

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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. It is also funded by the German Research Foundation (DFG) under reference number FR 3658/1-2 and by CAPES under reference number 99999.006033/2015-06, in the scope of the BRAGECRIM program.

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Authors and Affiliations

  1. Industrial and Systems Engineering Department, Federal University of Santa Catarina, Florianópolis, Brazil

    Satie L. Takeda-Berger & Enzo Morosini Frazzon

  2. Faculty of Production Engineering, University of Bremen, Bremen, Germany

    Eike Broda

  3. BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany

    Michael Freitag

Authors
  1. Satie L. Takeda-Berger
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  2. Enzo Morosini Frazzon
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  3. Eike Broda
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  4. Michael Freitag
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Corresponding author

Correspondence to Satie L. Takeda-Berger .

Editor information

Editors and Affiliations

  1. BIBA - Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany

    Michael Freitag

  2. Maritime Business and Logistics, University of Bremen, Bremen, Germany

    Hans-Dietrich Haasis

  3. Logistics Management, University of Bremen, Bremen, Germany

    Herbert Kotzab

  4. Department of Production Engineering, Universität Bremen, Bremen, Germany

    Jürgen Pannek

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Takeda-Berger, S.L., Frazzon, E.M., Broda, E., Freitag, M. (2020). Machine Learning in Production Scheduling: An Overview of the Academic Literature. In: Freitag, M., Haasis, HD., Kotzab, H., Pannek, J. (eds) Dynamics in Logistics. LDIC 2020. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-030-44783-0_39

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  • DOI: https://doi.org/10.1007/978-3-030-44783-0_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44782-3

  • Online ISBN: 978-3-030-44783-0

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