A DEA-compromise programming model for comprehensive ranking

This paper addresses comprehensive ranking systems determining an ordering of entities by aggregating quantitative data for multiple attributes. We propose a DEA-CP (Data Envelopment Analysis–Compromise Programming) model for the comprehensive ranking, including preference voting (ranked voting) to rank candidates in terms of aggregate vote by rank for each candidate. Although the DEA-CP model once employs the flexible DEA weighting system that can vary by entity, it finally aims at regressing to the common weights across the entities. Therefore, the model can totally rank the entities by specifying nothing arbitrary, and can avoid to use the diverse DEA weights.