A parametric GP model dealing with incomplete information for group decision-making

Dopazo, Esther; Ruiz-Tagle, Mauricio

doi:10.1016/j.amc.2011.05.094

Cited by 21 publications

(18 citation statements)

References 24 publications

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“…A (parametric) linear combination of the sum and the maximum is proposed by Jones and Mardle [24] to find a compromise weight vector. A similar idea is applied in the proposal of Dopazo and Ruiz-Tagle [14], developed for group decision problems with incomplete pairwise comparison matrices.…”

Section: Weighting As a Multiple Objective Optimization Problemmentioning

confidence: 99%

Efficient weight vectors from pairwise comparison matrices

Bozóki

Fülöp

2018

European Journal of Operational Research

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Pairwise comparison matrices are frequently applied in multi-criteria decision making. A weight vector is called efficient if no other weight vector is at least as good in approximating the elements of the pairwise comparison matrix, and strictly better in at least one position. A weight vector is weakly efficient if the pairwise ratios cannot be improved in all non-diagonal positions. We show that the principal eigenvector is always weakly efficient, but numerical examples show that it can be inefficient. The linear programs proposed test whether a given weight vector is (weakly) efficient, and in case of (strong) inefficiency, an efficient (strongly) dominating weight vector is calculated. The proposed algorithms are implemented in Pairwise Comparison Matrix Calculator, available at pcmc.online.Comment: 19 page

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Section: Weighting As a Multiple Objective Optimization Problemmentioning

confidence: 99%

Efficient weight vectors from pairwise comparison matrices

Bozóki

Fülöp

2018

European Journal of Operational Research

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“…The authors of [4] deal with this problem by means of similarity and parametric compromise functions. In [15], a fitness function is defined as a scalar vector function composed of the common error measure, based on the Euclidean distance, and a minimum violation error that accounts for no violation of the rank ordering is considered to improve deriving of the weights.…”

Section: Introductionmentioning

confidence: 99%

Pairwise comparisons simplified

Koczkodaj

Szybowski

2015

Applied Mathematics and Computation

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“…They are based on Saaty's assumption for MPR regarding the exact functional relation between the preference values and the priority vector. Two main approaches are used to develop indirect completion models based on the computation of the priority vector: linear based methods where the unknown variables are the elements of the weighting vector [23,35,81,83,88,94], and least square error minimization approaches [30,48,78,88].…”

Section: Optimisation and Linear Programming Based Methodsmentioning

confidence: 99%

“…Optimisation approaches to estimate the missing preference values or to directly rank the alternatives without previously completing the preference relations. Therefore there are two types of these approaches: 2.1 Methods that estimate the missing preferences [25,102], and 2.2 Methods that estimate the weighting vector [23,30,35,48,78,81,83,88,88,94].…”

Section: Decision Making Approaches With Incomplete Preferencesmentioning

confidence: 99%