An effective total ranking model for a ranked voting system

Foroughi, Ali Asghar; Tamiz, Mehrdad

doi:10.1016/j.omega.2004.07.013

Cited by 58 publications

(36 citation statements)

References 9 publications

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“…As a final comment, we point out that Foroughi and Tamiz [4] have extended Obata and Ishii's model to rank inefficient as well as efficient candidates. However, this extension continues having the same problems.…”

Section: Obata and Ishii's Model Using L 1 -Normmentioning

confidence: 92%

Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates

Llamazares

Peña

2009

European Journal of Operational Research

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a b s t r a c tThere are different ways to allow the voters to express their preferences on a set of candidates. In ranked voting systems, each voter selects a subset of the candidates and ranks them in order of preference. A well-known class of these voting systems are scoring rules, where fixed scores are assigned to the different ranks and the candidates with the highest score are the winners. One of the most important issues in this context is the choice of the scoring vector, since the winning candidate can vary according to the scores used. To avoid this problem, Cook and Kress [W.D. Cook, M. Kress, A data envelopment model for aggregating preference rankings, Management Science 36 (11) (1990) 1302-1310], using a DEA/AR model, proposed to assess each candidate with the most favorable scoring vector for him/her. However, the use of this procedure often causes several candidates to be efficient, i.e., they achieve the maximum score. For this reason, several methods to discriminate among efficient candidates have been proposed. The aim of this paper is to analyze and show some drawbacks of these methods.

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Section: Obata and Ishii's Model Using L 1 -Normmentioning

confidence: 92%

Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates

Llamazares

Peña

2009

European Journal of Operational Research

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“…To reach a consensus in this case requires the determination of the minimum information set that contains the preference relations elicited by the DMs, expressed in terms of bounds or inequalities of the components of the weighting vector. Similar proposals based in DEA are Hashimoto (1997), Obata and Ishii (2003) and Foroguchi and Tamiz (2005).…”

Section: The Modelmentioning

confidence: 99%

A Distance-Based Consensus Model with Flexible Choice of Rank-Position Weights

Contreras

2008

Group Decis Negot

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In this paper we propose a preference aggregation procedure for those cases in which the decision-makers express their preferences by means of a ranking of alternatives. Among the most applied methods for this purpose are those inspired by the Borda-Kendall rule, which attach to each alternative an aggregated value of the votes received in the different rank positions, and those based on distance measures between individual and collective preferences, which look for the solution that maximizes the consensus. The main idea here is to integrate these two approaches. Taking into account that the information about the values of weights or utilities assigned to each rank position is imprecise, we propose an evaluation of the alternatives using that vector of weights that minimizes the disagreement between DMs. In order to solve the problem, mixed-integer linear programming models are constructed. Two numerical examples are examined to illustrate the applicability of the proposed procedure.

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“…As such, the rank order presented using this method is independent of inefficient alternatives. Foroughi and Tamiz (2005) extended the model presented by Obata and Ishii (2003) in order to obtain the rank order for both efficient and inefficient alternatives. The extended model contains fewer constraints compared with the model introduced by Obata and Ishii (2003).…”

Section: Introductionmentioning

confidence: 99%

A novel three-stage distance-based consensus ranking method

Aghayi

Tavana

2018

J Ind Eng Int

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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.

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An effective total ranking model for a ranked voting system

Cited by 58 publications

References 9 publications

Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates

Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates

A Distance-Based Consensus Model with Flexible Choice of Rank-Position Weights

A novel three-stage distance-based consensus ranking method

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