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A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints

Author

Listed:
  • Dmitry Ivanov
  • Boris Sokolov
  • Weiwei Chen
  • Alexandre Dolgui
  • Frank Werner
  • Semyon Potryasaev

Abstract

We study the problem of scheduling in manufacturing environments which are dynamically reconfigurable for supporting highly flexible individual operation compositions of the jobs. We show that such production environments yield the simultaneous process design and operation sequencing with dynamically changing hybrid structural-logical constraints. We conceptualize a model to schedule jobs in such environments when the structural-logical constraints are changing dynamically and offer a design framework of algorithmic development to obtain a tractable solution analytically within the proven axiomatic of the optimal control and mathematical optimization. We further develop an algorithm to simultaneously determine the process design and operation sequencing. The algorithm is decomposition-based and leads to an approximate solution of the underlying optimization problem that is modeled by optimal control. We theoretically analyze the algorithmic complexity and apply this approach on an illustrative example. The findings suggest that our approach can be of value for modeling problems with a simultaneous process design and operation sequencing when the structural and logical constraints are dynamic and interconnected. Utilizing the outcomes of this research could also support the analysis of processing dynamics during the operations execution.

Suggested Citation

  • Dmitry Ivanov & Boris Sokolov & Weiwei Chen & Alexandre Dolgui & Frank Werner & Semyon Potryasaev, 2021. "A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints," IISE Transactions, Taylor & Francis Journals, vol. 53(1), pages 21-38, January.
    • Handle: RePEc:taf:uiiexx:v:53:y:2021:i:1:p:21-38
    DOI: 10.1080/24725854.2020.1739787
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    Citations

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    Cited by:

    1. Silvestro Vespoli & Guido Guizzi & Elisa Gebennini & Andrea Grassi, 2022. "A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture," Annals of Operations Research, Springer, vol. 310(1), pages 201-221, March.
    2. Brusset, Xavier & Ivanov, Dmitry & Jebali, Aida & La Torre, Davide & Repetto, Marco, 2023. "A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic," International Journal of Production Economics, Elsevier, vol. 263(C).
    3. Syed Abdul Rehman Khan & Muhammad Waqas & Xue Honggang & Naveed Ahmad & Zhang Yu, 2022. "Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic," Operations Management Research, Springer, vol. 15(3), pages 1115-1133, December.
    4. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    5. Andrea Maria Zanchettin, 2022. "Robust scheduling and dispatching rules for high-mix collaborative manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 293-316, June.
    6. Weiwei Chen & Siyang Gao & Wenjie Chen & Jianzhong Du, 2023. "Optimizing resource allocation in service systems via simulation: A Bayesian formulation," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 65-81, January.

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