Abstract
The paper deals with application of nontraditional DEA models for ranking of candidates in voting systems. The main aim of these systems is to find a general winner among all candidates and their complete ranking based on preferences of voters. Every voter gives a ranking of the first t-candidates according to his/her preferences. Traditional DEA models compare multiple inputs and outputs and estimate the relative efficiency of the units. For our purposes, we propose original DEA models without explicit inputs and outputs that express how many times the candidates are on certain rankings. Higher rankings are better and that is why the weights of the outputs must be distinguished. For this, DEA models with assurance region (DEA/AR) are applied. This paper deals with application of originally proposed DEA/AR models without explicit inputs and with their comparison. A numerical example with 11 candidates is used for illustrative purposes of the properties of the models. Its results for all presented models are compared especially with respect to the weights assigned to outputs.
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