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Collaborative optimization of workshop layout and scheduling

Author

Listed:
  • Yaliang Wang

    (Zhejiang University of Technology
    Zhejiang University of Technology)

  • Xinyu Fan

    (Zhejiang University of Technology)

  • Chendi Ni

    (Zhejiang University of Technology)

  • Kanghong Gao

    (Zhejiang University of Technology)

  • Shousong Jin

    (Zhejiang University of Technology
    Zhejiang University of Technology)

Abstract

The collaborative optimization of workshop layout and scheduling is key to realizing the efficient and orderly operation of manufacturing systems. To satisfy the low-entropy development mode and the urgent need for secondary development of enterprises, this study investigates the issue of collaborative optimization of workshop layout and scheduling by coupling and integrating their internal linkage. The low-entropy indexes of collaborative optimization of workshop layout and scheduling were analyzed, and the makespan, processing quality loss, and production cost were considered to be the optimization objectives. Accordingly, a low-entropy collaborative mathematical model of workshop layout and scheduling was constructed. Based on a multi-objective genetic algorithm for differential cell processes, an agent structure was introduced, and a new mutation strategy was designed. Considering the environmental disturbance factors, an agent cellular automata and differential evolution (ACADE) algorithm was proposed for solving the layout and scheduling coordination. Moreover, a case study was conducted, which provided basic theoretical methods and technical support for the coordinated optimization of workshop layout and scheduling.

Suggested Citation

  • Yaliang Wang & Xinyu Fan & Chendi Ni & Kanghong Gao & Shousong Jin, 2023. "Collaborative optimization of workshop layout and scheduling," Journal of Scheduling, Springer, vol. 26(1), pages 43-59, February.
    • Handle: RePEc:spr:jsched:v:26:y:2023:i:1:d:10.1007_s10951-022-00761-7
    DOI: 10.1007/s10951-022-00761-7
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    References listed on IDEAS

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    1. S Q Liu & E Kozan, 2012. "A hybrid shifting bottleneck procedure algorithm for the parallel-machine job-shop scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(2), pages 168-182, February.
    2. Hamid Beigy & M. R. Meybodi, 2004. "A Mathematical Framework For Cellular Learning Automata," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 7(03n04), pages 295-319.
    3. J. Behnamian, 2016. "Survey on fuzzy shop scheduling," Fuzzy Optimization and Decision Making, Springer, vol. 15(3), pages 331-366, September.
    4. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    5. Petrovic, Dobrila, 2001. "Simulation of supply chain behaviour and performance in an uncertain environment," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 429-438, May.
    6. Moursli, O. & Pochet, Y., 2000. "A branch-and-bound algorithm for the hybrid flowshop," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 113-125, March.
    7. Dunker, Thomas & Radons, Gunter & Westkamper, Engelbert, 2005. "Combining evolutionary computation and dynamic programming for solving a dynamic facility layout problem," European Journal of Operational Research, Elsevier, vol. 165(1), pages 55-69, August.
    8. Chuang Wang & Pingyu Jiang, 2018. "Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1485-1500, October.
    9. S. M. Johnson, 1954. "Optimal two‐ and three‐stage production schedules with setup times included," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(1), pages 61-68, March.
    10. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    11. Hariri, A. M. A. & Potts, C. N., 1997. "A branch and bound algorithm for the two-stage assembly scheduling problem," European Journal of Operational Research, Elsevier, vol. 103(3), pages 547-556, December.
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