A novel three-stage distance-based consensus ranking method

Abstract

In this study, we propose a three-stage weighted sum method for identifying the group ranks of alternatives. In the first stage, a rank matrix, similar to the cross-efficiency matrix, is obtained by computing the individual rank position of each alternative based on importance weights. In the second stage, a secondary goal is defined to limit the vector of weights since the vector of weights obtained in the first stage is not unique. Finally, in the third stage, the group rank position of alternatives is obtained based on a distance of individual rank positions. The third stage determines a consensus solution for the group so that the ranks obtained have a minimum distance from the ranks acquired by each alternative in the previous stage. A numerical example is presented to demonstrate the applicability and exhibit the efficacy of the proposed method and algorithms.

Three New Models for Ranking of Candidates In the Preferential Voting Systems

“…According to Aghayi and Tavana (2019), obtaining a group ranking or a winning candidate from individual's preferences on a set of alternatives is an important group decision problem with social choice and voting system implications. Nevertheless, several solutions have been proposed for solving this problem too.…”

“…When a group of people with individual preferences has to decide which alternative to choose from a given set of alternatives, an election is often carried out (Polykovskiy, Berghammer, & Neumann, 2016). Therefore, obtaining a group ranking or a winning candidate from individual's preferences on a set of alternatives is an important group decision problem with social choice and voting system implications (Aghayi & Tavana, 2019).…”

A sequence of targets toward a common best practice frontier in DEA

“…After that, some researchers generalized the idea of using Euclidean distance in order to define efficiency measure and also to project units onto the strongly efficient frontier, e.g., see Baek and Lee (2009), Amirteimoori and Kordrostami (2010), and Aparicio and Pastor (2014a, b). The issue of least distance to the frontier has been used in other applications of DEA, such as ranking units (e.g., see Ziari 2016;Aghayi and Tavana 2018). The concept of benchmarking based on least distance has also been developed by Pastor and Aparicio (2010), Ando et al (2012Ando et al ( , 2017, Aparicio andPastor (2013, 2014a, b) and Aparicio et al (2014).…”