A method for discriminating efficient candidates with ranked voting data

Obata, Tsuneshi; Ishii, Hiroaki

doi:10.1016/s0377-2217(02)00597-0

Cited by 111 publications

(76 citation statements)

References 6 publications

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“…Target value and allowable limits are set in order to cope with various criteria. Obata and Ishii also propose a new technique to deal with ranked voting data [24]. Any information about inefficient candidates are not included in the discrimination methods in order to avoid the instability of inefficient candidates.…”

Section: Issue Of Setting Boundsmentioning

confidence: 99%

Pairwise Comparison for Weight Restriction in DEA/ARI

Arunyanart

Ohmori²,

Yoshimoto³

2015

Int J Jpn Assoc Mgt Sys

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This paper proposes an approach for determining weight restriction constrains in assurance region method by applying pairwise comparison technique to convert judgment of expert on relative importance among decision criteria into weight bound values which are in a form of ratios of weights. The proposed approach has an attempt to simplify setting bounds which could also give the result to be more consistent with the direction of decision maker. The method is illustrated in facility location problem.

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Section: Issue Of Setting Boundsmentioning

confidence: 99%

Pairwise Comparison for Weight Restriction in DEA/ARI

Arunyanart

Ohmori²,

Yoshimoto³

2015

Int J Jpn Assoc Mgt Sys

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“…Noguchi et al (2002) presented a strong ordering to alternatives in which weights are obtained using the feasible solution region of the constraint set in LP. Obata and Ishii (2003) introduced a method to discriminate efficient alternatives using ranked voting data without considering information about inefficient alternatives. As such, the rank order presented using this method is independent of inefficient alternatives.…”

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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“…However, one important shortcoming of their model is that several candidates are often efficient, i.e., they achieve the maximum attainable score. For this reason, some procedures to discriminate efficient candidates have appeared in the literature (see, for instance, Green et al (1996), Hashimoto (1997), Noguchi et al (2002) and Obata and Ishii (2003)). Nevertheless, as it has been noticed by Llamazares and Peña (2009), some of the previous models have a serious drawback from the point of view of Social Choice Theory: the relative order between two candidates may be altered when the number of first, second, .…”

Section: Introductionmentioning

confidence: 99%

Ranking Candidates Through Convex Sequences of Variable Weights

Llamazares

2015

Group Decis Negot

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Scoring rules are a well-known class of positional voting systems where fixed scores are assigned to the different ranks. Nevertheless, since the winners may change according to the scores used, the choice of the scoring vector is not obvious. For this reason several methods have been suggested so that each candidate may be evaluated with the most favorable scoring vector for him/her. In this paper we propose a new model that allows to use different scoring vector for each candidate and avoid some shortcomings of other methods suggested in the literature. Moreover we give a closed expression for the score obtained by each candidate and, in this way, it is possible to rank the candidates without solving the proposed model.

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A method for discriminating efficient candidates with ranked voting data

Cited by 111 publications

References 6 publications

Pairwise Comparison for Weight Restriction in DEA/ARI

Pairwise Comparison for Weight Restriction in DEA/ARI

A novel three-stage distance-based consensus ranking method

Ranking Candidates Through Convex Sequences of Variable Weights

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