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Job shop planning and scheduling of reconfigurable manufacturing systems

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Abstract

The fourth industrial revolution (Industry 4.0) has enabled rapid product variations and technological developments such as reconfigurable manufacturing systems (RMSs). Planning and scheduling in RMS differ from traditional systems; therefore, the manufacturing industry has faced implementation barriers. This research proposes a new mixed-integer linear programming (MILP) formulation for process planning and scheduling in RMSs. The formulation, considers new aspects such as number of products, their quantity and complexity, calibration rate, work-in-process (WIP), and inventory management. Moreover, this research promotes RMS effectiveness for business and provides new insights on the effects of different factors on the overall performance. The applicability of the proposed formulation is illustrated with a case study. The results showed that RMS outperforms a traditional system with 30% savings in cost and up to a 25% increase in demand fulfillment. Sensitivity analyses are conducted to investigate the effect of different parameters on the cost-effectiveness of RMS compared to traditional manufacturing systems. Analysis of variance (ANOVA) highlighted the importance of the reconfigurability of the machines compared to other settings

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  • Industrial and Production Engineering
  • Manufacturing Organization
  • Mathematical Modeling and Industrial Mathematics
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Data Availability

Data sets generated during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors wish to express their profound gratitude to Osama Aydas, Vijitashwa Pandey, and Ka C. Cheok for their invaluable guidance and support while developing the thesis, “Stochastic Planning and Scheduling for Reconfigurable Job Shops and Flow Lines”. The thesis served as a foundational reference that has guided and shaped the development of this presented research. Additionally, we acknowledge the reviewers for their meticulous and insightful comments, which have significantly enhanced the quality of this manuscript. Their thorough evaluation and constructive feedback were instrumental in refining our work and ensuring its academic rigor.

Funding

This study was funded by Oakland University.

Author information

Authors and Affiliations

  1. Industrial and System Engineering, Oakland University, 115 Library Dr, Rochester, MI, 48309, USA

    Jad Imsetif & Nasim Nezamoddini

  2. Department of Industrial Engineering, The University of Louisville, 132 Eastern Pkwy, Louisville, KY, 40292, USA

    Faisal Aqlan

Corresponding author

Correspondence to Jad Imsetif.

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Conflicts of Interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

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Cite this article

Imsetif, J., Nezamoddini, N. & Aqlan, F. Job shop planning and scheduling of reconfigurable manufacturing systems. Oper Manag Res (2025). https://doi.org/10.1007/s12063-025-00551-2

  • Received
  • Revised
  • Accepted
  • Published
  • DOI https://doi.org/10.1007/s12063-025-00551-2

Keywords

  • Reconfigurable manufacturing systems
  • Mixed integer linear programming
  • Optimization
  • Sensitivity analyses
  • Cost effectiveness

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