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A MILP model for the long term academic staff size and composition planning in public universities

Author

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  • de la Torre, R.
  • Lusa, A.
  • Mateo, M.

Abstract

This paper proposes a model for dealing with the long term staff composition planning in public universities. University academic staff is organized in units (or departments) according to their field of expertize. The staff for each unit is distributed in a set of categories, each one characterized by their teaching hours, cost and other specificities. Besides the use for planning (and updating a plan), the model can be used to assess the impact that different strategies may have on the personnel costs and the structure of a university. The proposed model is formulated generally, so it can be applied to different types of universities attending to their characteristics. The model is applied to a real case and validated by means of a computational experiment considering several scenarios. The analysis is focused on achieving a preferable academic staff composition under service level constraints while also minimizing the associated economic expenditures considering a long term horizon. The results show that the model successes in approaching the staff composition to a previously defined pattern preferable one.

Suggested Citation

  • de la Torre, R. & Lusa, A. & Mateo, M., 2016. "A MILP model for the long term academic staff size and composition planning in public universities," Omega, Elsevier, vol. 63(C), pages 1-11.
    • Handle: RePEc:eee:jomega:v:63:y:2016:i:c:p:1-11
    DOI: 10.1016/j.omega.2015.09.008
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    References listed on IDEAS

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    Cited by:

    1. N. Llort & A. Lusa & C. Martínez-Costa & M. Mateo, 2019. "A decision support system and a mathematical model for strategic workforce planning in consultancies," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 497-523, June.
    2. da Cunha, Joaquim J. & de Souza, Mauricio C., 2018. "A linearized model for academic staff assignment in a Brazilian university focusing on performance gain in quality indicators," International Journal of Production Economics, Elsevier, vol. 197(C), pages 43-51.
    3. Cardoso-Grilo, Teresa & Monteiro, Marta & Oliveira, Mónica Duarte & Amorim-Lopes, Mário & Barbosa-Póvoa, Ana, 2019. "From problem structuring to optimization: A multi-methodological framework to assist the planning of medical training," European Journal of Operational Research, Elsevier, vol. 273(2), pages 662-683.
    4. Rocio de la Torre & Jasmina Berbegal-Mirabent, 2022. "Knowledge-Intensive Organizations and Their Role in Promoting Sustainable Development," Administrative Sciences, MDPI, vol. 12(3), pages 1-3, August.

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