Two effective total ranking models for preference voting and aggregation

Data envelopment analysis (DEA), a useful assessment tool, has been used to solve the problem of preference voting and aggregation which requires the determination of the weights associated with different ranking places. Instead of applying the same externally imposed weighting scheme to all candidates, DEA models allow each candidate to choose his/her own weights to maximize his/her own overall ratings subject to certain conditions. This paper proposes two new models to assess the weights. The proposed models are linear programming, which determine a common set of weights for all the candidates. The proposed models are examined with two numerical examples and it is shown that the proposed models can not only choose a winner, but also give a full ranking of all the candidates..