دانلود رایگان مقاله یک مدل MILP برای برنامه ریزی طولانی مدت کارکنان دانشگاهی و برنامه ریزی ترکیب در دانشگاه های دولتی

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.

6. Conclusions and future research

This work presents a mixed linear mathematical programming model for determining the size and composition of the academic staff of public universities under a long term planning horizon and taking into account the category structure and a preferable composition, while minimizing the associated costs. The problem, which is relevant and very important for the performance of any public university, is too difficult to be solved without an adequate and formalised procedure and powerful tools and techniques (as MILP is). The particular case of the Universitat Politècnica de Catalunya (UPC) has been chosen to apply the model to a real case. The designed model successes in obtaining a close composition to a preferable one taking into account constraints associated to budget and required service level. In particular, the Global Discrepancy, which considers the preferable workforce composition, has been reduced up to 95% for temporary categories, 97% for permanent (non-public) categories and 100% for permanent public categories. Also, the model is tested in a wide experiment to obtain both computational and managerial insights.