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

• Published in: Omega (Oxford) Vol. 63; pp. 1 - 11
• Main Authors: de la Torre, R., Lusa, A., Mateo, M.
• Format: Journal Article Publication
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2016; Pergamon Press Inc
• Subjects: Colleges & universities; Economia i organització d'empreses; Integer programming; KIBS; KIO/KIF; Long term; Long Term St aff Planning; Long term staff planning; MILP; Organizational structure; Planificació; Planning; Public universities and colleges; Strategic Staff Planning; Universitats; University faculty; Validation studies; Workforce planning; Àrees temàtiques de la UPC; MILP; KIBS; Long term staff planning; Strategic staff planning; KIO/KIF
• ISSN: 0305-0483; 1873-5274
• DOI: 10.1016/j.omega.2015.09.008

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. •We present a formalized procedure for the strategic staff planning in universities.•Cost, preferable staff composition and service level determine the workforce.•The error between initial and preferable staff composition is nearly reduced 100%.