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Proactive Operations Management: Staff Allocation with Competence Maintenance Constraints

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  • Eryk Szwarc

    (Faculty of Electronics and Computer Science, Koszalin University of Technology, ul. Śniadeckich 2, 75-453 Koszalin, Poland)

  • Grzegorz Bocewicz

    (Faculty of Electronics and Computer Science, Koszalin University of Technology, ul. Śniadeckich 2, 75-453 Koszalin, Poland)

  • Paulina Golińska-Dawson

    (Faculty of Engineering Management, Poznań University of Technology, ul. Jacka Rychlewskiego 2, 60-965 Poznań, Poland)

  • Zbigniew Banaszak

    (Faculty of Electronics and Computer Science, Koszalin University of Technology, ul. Śniadeckich 2, 75-453 Koszalin, Poland)

Abstract

Highly qualified staff are the key to successful operations management in any organization. In this paper, the emphasis is put on the problem of planning the rotational assignment of work tasks to a multi-skilled staff to guarantee maintaining their competencies at the required level. The aim of this study is to propose a novel declarative model for proactive planning of staff allocation whilst taking into account the forgetting effect. Sufficient conditions are proposed that allow for the cyclical rotation of employees between different tasks in order to keep their competencies at a constant level. The numerical experiments prove that the presented approach allows for finding a trade-off between a robustness to absenteeism and maintaining staff competency levels. The proposed method is suitable for human resource-related decision making in an interactive mode.

Suggested Citation

  • Eryk Szwarc & Grzegorz Bocewicz & Paulina Golińska-Dawson & Zbigniew Banaszak, 2023. "Proactive Operations Management: Staff Allocation with Competence Maintenance Constraints," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1949-:d:1041616
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    References listed on IDEAS

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    1. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 115512, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Steven Hoedt & Arno Claeys & El-Houssaine Aghezzaf & Johannes Cottyn, 2020. "Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
    3. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "Workforce planning incorporating skills: State of the art," European Journal of Operational Research, Elsevier, vol. 243(1), pages 1-16.
    4. Glock, C. H. & Jaber, M. Y., 2013. "A multi-stage production-inventory model with learning and forgetting effects, rework and scrap," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 59034, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Domenech, B & Lusa, A, 2016. "A MILP model for the teacher assignment problem considering teachers’ preferences," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1153-1160.
    6. Thomas Felberbauer & Walter J. Gutjahr & Karl F. Doerner, 2019. "Stochastic project management: multiple projects with multi-skilled human resources," Journal of Scheduling, Springer, vol. 22(3), pages 271-288, June.
    7. Jaber, Mohamad Y. & Sikstrom, Sverker, 2004. "A numerical comparison of three potential learning and forgetting models," International Journal of Production Economics, Elsevier, vol. 92(3), pages 281-294, December.
    8. Jonas Ingels & Broos Maenhout, 2017. "Employee substitutability as a tool to improve the robustness in personnel scheduling," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 623-658, July.
    9. Sebnem Demirkol Akyol & Adil Baykasoğlu, 2019. "ErgoALWABP: a multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem under ergonomic risk factors," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 291-302, January.
    10. Grzegorz Drozdowski & Joanna Rogozińska-Mitrut & Jacek Stasiak, 2021. "The Empirical Analysis of the Core Competencies of the Company’s Resource Management Risk. Preliminary Study," Risks, MDPI, vol. 9(6), pages 1-12, June.
    11. Juan Herrera & Carlos de las Heras-Rosas, 2020. "Corporate Social Responsibility and Human Resource Management: Towards Sustainable Business Organizations," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    12. Hewitt, Mike & Chacosky, Austin & Grasman, Scott E. & Thomas, Barrett W., 2015. "Integer programming techniques for solving non-linear workforce planning models with learning," European Journal of Operational Research, Elsevier, vol. 242(3), pages 942-950.
    13. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 115511, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    14. Sabina Asensio-Cuesta & Juan M. García-Gómez & José-Luis Poza-Luján & J. Alberto Conejero, 2019. "A Game-Theory Method to Design Job Rotation Schedules to Prevent Musculoskeletal Disorders Based on Workers’ Preferences and Competencies," IJERPH, MDPI, vol. 16(23), pages 1-16, November.
    15. Ingels, Jonas & Maenhout, Broos, 2019. "Optimised buffer allocation to construct stable personnel shift rosters," Omega, Elsevier, vol. 82(C), pages 102-117.
    16. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 107692, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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