In this paper, we present logic-based aggregation models used for ranking student applicants and we compare them with a number of existing aggregation methods, each more complex than the previous one. The proposed models aim to include dependencies in the data using Logical aggregation (LA). | Yugoslav Journal of Operations Research 27 (2017), Number 4, 463–479 DOI: LOGIC-BASED AGGREGATION METHODS FOR RANKING STUDENT APPLICANTS ˇ ´ Pavle MILOSEVI C Faculty of Organizational Sciences, University of Belgrade, Serbia ˇC ´ Ivan NESI Faculty of Organizational Sciences, University of Belgrade, Serbia Ana POLEDICA Faculty of Organizational Sciences, University of Belgrade, Serbia ´ Dragan RADOJEVIC Mihajlo Pupin Institute, Belgrade, Serbia ´ Bratislav PETROVIC Faculty of Organizational Sciences, University of Belgrade, Serbia Received: November 2016 / Accepted: May 2017 Abstract: In this paper, we present logic-based aggregation models used for ranking student applicants and we compare them with a number of existing aggregation methods, each more complex than the previous one. The proposed models aim to include dependencies in the data using Logical aggregation (LA). LA is a aggregation method based on interpolative Boolean algebra (IBA), a consistent multi-valued realization of Boolean algebra. This technique is used for a Boolean consistent aggregation of attributes that are logically dependent. The comparison is performed in the case of student applicants for master programs at the University of Belgrade. We have shown that LA has some advantages over other presented aggregation methods. The software realization of all applied aggregation methods is also provided. This paper may be of interest not only for student ranking, but also for similar problems of ranking people . employees, team members, etc. 464 P. Miloˇsevi´c, et al. / Logic-based aggregation methods for ranking student Keywords: Aggregation, Logical aggregation, Interpolative Boolean Algebra, Fuzzy Logic, Ranking Students. MSC: 47S40. 1. INTRODUCTION This paper examines the problem of ranking people in multi-attribute
