Aggregation operators for selection problems

Smolikova, R.; Wachowiak, Mark P.

doi:10.1016/s0165-0114(01)00252-4

Cited by 76 publications

(26 citation statements)

References 10 publications

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“…After comparing the operators in [4,8,11,20,23,27,28,32] with the OWA operator, the paper finds that the OWA operator has the rational aggregation result, and more closely fits the thoughts of human beings (between the "and" and "or" situations) [27]. Moreover, under the circumstances of maximal information entropy, the OWA operator can get the optimum result of the aggregation.…”

Section: Dynamic Fuzzy Owa Modelmentioning

confidence: 86%

“…For example, Fuller and Majlender [8] have used Lagrange multipliers to derive a polynomial equation to solve constrained optimization problem and to determine the optimal weighting vector. Meanwhile, Smolikova and Wachowiak [23] have described and compared aggregation techniques for expert multi-criteria decision-making method. Furthermore, Ribeiro and Pereira [20] have presented an aggregation schema based on generalized mixture operators using weighting functions, and have compared it with these two standard aggregation method: weighting averaging and ordered weighted averaging in the context of multiple attribute decision making.…”

Section: Owa Operatormentioning

confidence: 99%

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Dynamic fuzzy OWA model for group multiple criteria decision making

Chang

Cheng

et al. 2005

Soft Comput

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Obtaining relative weights in MCDM problems is a very important issue. The Ordered Weighted Averaging (OWA) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators (including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model -dynamic fuzzy OWA based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a "magnifying lens" to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. Two examples are adopted in this paper for comparison and showing the effects under different weights.

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Section: Dynamic Fuzzy Owa Modelmentioning

confidence: 86%

Section: Owa Operatormentioning

confidence: 99%

Dynamic fuzzy OWA model for group multiple criteria decision making

Chang

Cheng

et al. 2005

Soft Comput

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“…, which is usually a continuous and symmetric function (Klir & Folger, 1988;Smolíková & Wachowiak, 2002). The monotonicity of the aggregation operator is a crucial issue which involves constraints on the derivative of the weighted aggregation operator with respect to the various attribute preference values.…”

Section: Aggregation and Weighted Generalized Meansmentioning

confidence: 99%

A weighted linear combination ranking technique for multi-criteria decision analysis

Chou¹

2013

SAJEMS

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Multi-criteria decision analysis (MCDA) is an alternative approach, which provides a way to systematically structure and analyse complex decision problems. This study presents a novel method of applying the weighted linear combination ranking technique (WLCRT) to MCDA. The proposed WLCRT method is based on the linear combinations of matrix algebra calculations. It has distinct advantages in preference modeling, weight elicitation, and aggregation performance. In this method, the decision matrix of preferences is constructed using a 7-point Likert scale. The weights of criteria are elicited from the proximity matrix of preference relations using the eigenvector method. Then, the weighted generalised means are used to aggregate preference information as well as to rank the order of decision alternatives. The WLCRT method can flexibly reflect different decision attitudes for the decision maker. It is both technically valid and practically useful, and can be used in dealing with multiple criteria analysis problems involving ranking of alternatives.

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“…I denotes the set of values to be aggregated and J denotes the corresponding result of the aggregation [9]. OWA operator was introduced in 1988 by Yager [10][11][12].…”

Section: Owa Operatormentioning

confidence: 99%

Application�of�multi�attribute�decision�making�methods�to�resources�allocation�problems�

Mianabadi

Giesen

Mostert

et al. 2012

SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings

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Operation of over shared resources (water, gas, oil, and mineral reserves) has been one the most significant challenges of states. "Fair" and "efficient" national resources reallocation among stakeholders and states is a complex conflict problem that faces this fundamental question: which criteria and mechanisms should be taken into account for this reallocation? In this paper, we propose a risk-based Multi Attribute Decision Making (MADM) methodology to select the most appropriate mechanism for reallocation of the reserves of the Caspian Sea with respect to several quantitative and qualitative criteria. Caspian Sea is a sea with five claimants that border it, Azerbaijan, Iran, Kazakhstan, Russia, and Turkmenistan. The ordered weighted averaging (OWA) method is used to evaluate the effects of risk attitude of the decision makers on the final outcome in resources reallocation. Results indicate that risk-based MADM methods are well suited tools to resolve conflicts in natural resources reallocation problems.

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Aggregation operators for selection problems

Cited by 76 publications

References 10 publications

Dynamic fuzzy OWA model for group multiple criteria decision making

Dynamic fuzzy OWA model for group multiple criteria decision making

A weighted linear combination ranking technique for multi-criteria decision analysis

Application�of�multi�attribute�decision�making�methods�to�resources�allocation�problems�

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