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A two-stage solution approach for personalized multi-department multi-day shift scheduling

Author

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  • Dahmen, Sana
  • Rekik, Monia
  • Soumis, François
  • Desaulniers, Guy

Abstract

In this paper, we address a personalized multi-department multi-day shift scheduling problem with a multi-skill heterogeneous workforce where employees can be transferred between departments under some restrictions. The objective is to construct a schedule that minimizes under-coverage, over-coverage, transfer and labor costs. We propose a novel two-stage approach to solve it: the first stage considers an approximate and smaller problem based on data aggregation and produces approximate transfers. The second stage constructs personalized schedules based on the information deduced from the first stage. An exhaustive experimental study is conducted and proves the efficiency of the proposed approach in terms of solution quality and computing times.

Suggested Citation

  • Dahmen, Sana & Rekik, Monia & Soumis, François & Desaulniers, Guy, 2020. "A two-stage solution approach for personalized multi-department multi-day shift scheduling," European Journal of Operational Research, Elsevier, vol. 280(3), pages 1051-1063.
    • Handle: RePEc:eee:ejores:v:280:y:2020:i:3:p:1051-1063
    DOI: 10.1016/j.ejor.2019.07.068
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    Cited by:

    1. Mansini, Renata & Zanella, Marina & Zanotti, Roberto, 2023. "Optimizing a complex multi-objective personnel scheduling problem jointly complying with requests from customers and staff," Omega, Elsevier, vol. 114(C).
    2. A.C. Mahasinghe & L.A. Sarathchandra, 2020. "An Optimization Model for Production Planning in the Synthetic Fertilizer Industry," International Association of Decision Sciences, Asia University, Taiwan, vol. 24(3), pages 28-62, September.
    3. Ghazaleh Ahmadi & Reza Tavakkoli-Moghaddam & Armand Baboli & Mehdi Najafi, 2022. "A decision support model for robust allocation and routing of search and rescue resources after earthquake: a case study," Operational Research, Springer, vol. 22(2), pages 1039-1081, April.
    4. A.C. Mahasinghe & L.A. Sarathchandra, 2020. "An Optimization Model for Production Planning in the Synthetic Fertilizer Industry," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(3), pages 28-62, September.

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