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Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications

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  • Alexandre Dolgui
  • Dmitry Ivanov
  • Suresh P. Sethi
  • Boris Sokolov

Abstract

This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal–logical constraints with applications to short term job and flow shop scheduling, and hybrid structural–terminal–logical constraints with applications to customised assembly systems such as Industry 4.0. Computational algorithms in state, control and adjoint variable spaces are discussed.

Suggested Citation

  • Alexandre Dolgui & Dmitry Ivanov & Suresh P. Sethi & Boris Sokolov, 2019. "Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications," International Journal of Production Research, Taylor & Francis Journals, vol. 57(2), pages 411-432, January.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:2:p:411-432
    DOI: 10.1080/00207543.2018.1442948
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    Cited by:

    1. Sheshadri Chatterjee & Ranjan Chaudhuri & Sachin Kamble & Shivam Gupta & Uthayasankar Sivarajah, 2023. "Adoption of Artificial Intelligence and Cutting-Edge Technologies for Production System Sustainability: A Moderator-Mediation Analysis," Information Systems Frontiers, Springer, vol. 25(5), pages 1779-1794, October.
    2. Sony, Michael & Antony, Jiju & Mc Dermott, Olivia & Garza-Reyes, Jose Arturo, 2021. "An empirical examination of benefits, challenges, and critical success factors of industry 4.0 in manufacturing and service sector," Technology in Society, Elsevier, vol. 67(C).
    3. Guoqing Zhang & Yiqin Yang & Guoqing Yang, 2023. "Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America," Annals of Operations Research, Springer, vol. 322(2), pages 1075-1117, March.
    4. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    5. Carlos A. Moreno-Camacho & Jairo R. Montoya-Torres & Anicia Jaegler, 2023. "Sustainable supply chain network design: a study of the Colombian dairy sector," Annals of Operations Research, Springer, vol. 324(1), pages 573-599, May.
    6. Gillani, Fatima & Chatha, Kamran Ali & Sadiq Jajja, Muhammad Shakeel & Farooq, Sami, 2020. "Implementation of digital manufacturing technologies: Antecedents and consequences," International Journal of Production Economics, Elsevier, vol. 229(C).
    7. Usama Awan & Robert Sroufe & Muhammad Shahbaz, 2021. "Industry 4.0 and the circular economy: A literature review and recommendations for future research," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2038-2060, May.
    8. Guilherme F. Frederico, 2021. "Project Management for Supply Chains 4.0: A conceptual framework proposal based on PMBOK methodology," Operations Management Research, Springer, vol. 14(3), pages 434-450, December.
    9. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    10. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    11. Ke-Liang Wang & Ting-Ting Sun & Ru-Yu Xu, 2023. "The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises," Economic Change and Restructuring, Springer, vol. 56(2), pages 1113-1146, April.
    12. Lilan Liu & Kai Guo & Zenggui Gao & Jiaying Li & Jiachen Sun, 2022. "Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    13. 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).
    14. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Meyer, Patrick & Dolgui, Alexandre, 2023. "Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic," Omega, Elsevier, vol. 120(C).
    15. Estefania Tobon-Valencia & Samir Lamouri & Robert Pellerin & Alexandre Moeuf, 2022. "Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0," Sustainability, MDPI, vol. 14(19), pages 1-28, October.

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