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Production scheduling in the context of Industry 4.0: review and trends

Author

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  • Manuel Parente
  • Gonçalo Figueira
  • Pedro Amorim
  • Alexandra Marques

Abstract

Notwithstanding its disruptive potential, which has been the object of considerable debate, Industry4.0 (I4.0) operationalisation still needs significant study. Specifically, scheduling is a key process that should be explored from this perspective. The purpose of this study is to shed light on the issues regarding scheduling that need to be considered in the new I4.0 framework. To achieve this, a two-stage cascade literature review is performed. The review begins with an analysis regarding the opportunities and challenges brought by I4.0 to the scheduling field, outputting a set of critical scheduling areas (CSA) in which development is essential. The second-stage literature review is performed to understand which steps have been taken so far by previous research in the scheduling field to address those challenges. Thus, a first contribution of this work is to provide insight on the influence and expected changes brought by I4.0 to scheduling, while showcasing relevant research. Another contribution is to identify the most promising future lines of research in this field, in which relevant challenges such as holistic scheduling, or increased flexibility requirements are highlighted. Concurrently, CSA such as decentralised decision-making, and human–robot collaboration display large gaps between current practice and the required technological level of development.

Suggested Citation

  • Manuel Parente & Gonçalo Figueira & Pedro Amorim & Alexandra Marques, 2020. "Production scheduling in the context of Industry 4.0: review and trends," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5401-5431, September.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:17:p:5401-5431
    DOI: 10.1080/00207543.2020.1718794
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    Citations

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    Cited by:

    1. Pourya Pourhejazy & Chen-Yang Cheng & Kuo-Ching Ying & Nguyen Hoai Nam, 2023. "Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups," Annals of Operations Research, Springer, vol. 322(1), pages 125-146, March.
    2. Tortorella, Guilherme Luz & Saurin, Tarcisio A. & Hines, Peter & Antony, Jiju & Samson, Daniel, 2023. "Myths and facts of industry 4.0," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
    4. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    5. Behice Meltem Kayhan & Gokalp Yildiz, 2023. "Reinforcement learning applications to machine scheduling problems: a comprehensive literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 905-929, March.
    6. Núñez-Merino, Miguel & Maqueira-Marín, Juan Manuel & Moyano-Fuentes, José & Castaño-Moraga, Carlos Alberto, 2022. "Industry 4.0 and supply chain. A Systematic Science Mapping analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    7. Lemstra, Mary Anny Moraes Silva & de Mesquita, Marco Aurélio, 2023. "Industry 4.0: a tertiary literature review," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    8. Dieste, Marcos & Sauer, Philipp C. & Orzes, Guido, 2022. "Organizational tensions in industry 4.0 implementation: A paradox theory approach," International Journal of Production Economics, Elsevier, vol. 251(C).
    9. Timon Hoebert & Wilfried Lepuschitz & Markus Vincze & Munir Merdan, 2023. "Knowledge-driven framework for industrial robotic systems," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 771-788, February.

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