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Joint decision-making for divisional seru scheduling and worker assignment considering process sequence constraints

Author

Listed:
  • Lili Wang

    (Nanjing University)

  • Min Li

    (Nanjing University
    Civil Aviation Flight University of China)

  • Guanbin Kong

    (Nanjing University)

  • Haiwen Xu

    (Civil Aviation Flight University of China)

Abstract

This paper concentrates on the divisional seru scheduling and worker assignment joint decision-making problem, and synthetically considers the difference in workers’ skill sets, the diversity of workers’ skill levels, the process sequence constraints, setup time, lot-splitting, etc., and then a nonlinear integer programming model is constructed to minimize the makespan. We show that it is necessary to consider the process sequence constraints, and the optimal makespan of the worker-operation allocation scheme without considering the process sequence constraints is much larger than that of considering the process sequence constraints. Moreover, as the number of workers increases, the advantage of considering sequence constraints becomes more obvious. Considering the multi-decision attributes and intractable computations of the studied problem, we turn it into bi-level programming. Then based on the combination of a hybrid genetic variable neighbourhood search algorithm (HGVNSA) and a greedy heuristic algorithm (GHA), a bi-level nested heuristic algorithm (HGVNSA-GHA) is designed. Finally, numerical experiment results show that the proposed algorithm can achieve better results and higher efficiency for the divisional seru scheduling and worker assignment model.

Suggested Citation

  • Lili Wang & Min Li & Guanbin Kong & Haiwen Xu, 2024. "Joint decision-making for divisional seru scheduling and worker assignment considering process sequence constraints," Annals of Operations Research, Springer, vol. 338(2), pages 1157-1185, July.
    • Handle: RePEc:spr:annopr:v:338:y:2024:i:2:d:10.1007_s10479-024-05983-w
    DOI: 10.1007/s10479-024-05983-w
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    References listed on IDEAS

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    1. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    2. Rapeepan Pitakaso & Kanchana Sethanan & Ganokgarn Jirasirilerd & Paulina Golinska-Dawson, 2023. "A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types," Annals of Operations Research, Springer, vol. 324(1), pages 1501-1525, May.
    3. Zhang, Zhe & Song, Xiaoling & Huang, Huijung & Zhou, Xiaoyang & Yin, Yong, 2022. "Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect," European Journal of Operational Research, Elsevier, vol. 297(3), pages 866-877.
    4. Aleda Roth & Jaya Singhal & Kalyan Singhal & Christopher S. Tang, 2016. "Knowledge Creation and Dissemination in Operations and Supply Chain Management," Production and Operations Management, Production and Operations Management Society, vol. 25(9), pages 1473-1488, September.
    5. Zhe Zhang & Xiaoling Song & Xue Gong & Yong Yin & Benjamin Lev & Xiaoyang Zhou, 2023. "An effective heuristic based on 3-opt strategy for seru scheduling problems with learning effect," International Journal of Production Research, Taylor & Francis Journals, vol. 61(6), pages 1938-1954, March.
    6. Yong Yin & Kathryn E. Stecke & Dongni Li, 2018. "The evolution of production systems from Industry 2.0 through Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 848-861, January.
    7. Yujing Jiang & Zhe Zhang & Xiaoling Song & Yong Yin, 2021. "Scheduling controllable processing time jobs in seru production system with resource allocation," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(11), pages 2551-2571, December.
    8. Zhe Zhang & Lili Wang & Xiaoling Song & Huijun Huang & Yong Yin, 2022. "Improved genetic-simulated annealing algorithm for seru loading problem with downward substitution under stochastic environment," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1800-1811, August.
    9. Chang Liu & Zhen Li & Jiafu Tang & Xuequn Wang & Ming-Jong Yao, 2022. "How SERU production system improves manufacturing flexibility and firm performance: an empirical study in China," Annals of Operations Research, Springer, vol. 316(1), pages 529-554, September.
    10. Jack Brimberg & Nenad Mladenović & Raca Todosijević & Dragan Urošević, 2019. "Solving the capacitated clustering problem with variable neighborhood search," Annals of Operations Research, Springer, vol. 272(1), pages 289-321, January.
    11. Yu, Yang & Tang, Jiafu & Gong, Jun & Yin, Yong & Kaku, Ikou, 2014. "Mathematical analysis and solutions for multi-objective line-cell conversion problem," European Journal of Operational Research, Elsevier, vol. 236(2), pages 774-786.
    12. Zhe Zhang & Xiaoling Song & Huijun Huang & Yong Yin & Benjamin Lev, 2022. "Scheduling problem in seru production system considering DeJong’s learning effect and job splitting," Annals of Operations Research, Springer, vol. 312(2), pages 1119-1141, May.
    13. Li, Dongni & Jiang, Yuzhou & Zhang, Jinhui & Cui, Zihua & Yin, Yong, 2023. "An on-line seru scheduling algorithm with proactive waiting considering resource conflicts," European Journal of Operational Research, Elsevier, vol. 309(2), pages 506-515.
    14. Kathryn E. Stecke & Yong Yin & Ikou Kaku & Yasuhiko Murase, 2012. "Seru: The Organizational Extension of JIT for a Super-Talent Factory," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 3(1), pages 106-119, January.
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