Linguistic labels for expressing fuzzy preference relations in fuzzy group decision making

Marimin, Marimin; Umano, Motohide; Hatono, Itsuo; Tamura, Hiroyuki

doi:10.1109/3477.662760

Cited by 122 publications

(48 citation statements)

References 20 publications

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“…Two lines of reasoning are available in this methodology to solve the pairwise group decision making problem: a direct approach and an indirect approach [43] [47]. Marimin et al extended this methodology including the direct approach and the indirect approach by allowing the decision makers to express their preference relations in linguistic labels rather than in numerical values [61]. In this approach, preference relations are represented internally using triangular fuzzy numbers and unordered preference relations are allowed to be aggregated by using the neat OWA operators [96].…”

Section: B Methodologies Of Group Decision Making Under Fuzzinessmentioning

confidence: 99%

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

John

Zhou²,

Garibaldi

et al. 2008

IJCIR

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Abstract:In the real world, group decision making is one of the most significant and omnipresent human decision making activities. The central problem of group decision making is to develop "fair" methods for aggregating individual alternatives (options, variants, etc.) to yield a consensus decision that is most acceptable to the group as a whole. It has become a subject of intensive research due to its practical and academic significance. But group decision making is innately uncertain, so a promising framework of decision making in groups is to consider uncertainty measures in the processes of model construction, so as to achieve optimal solutions in terms of best degrees of consensus of final decisions. This paper aims to provide an in-depth overview of group decision making incorporating uncertainty from the perspective of fuzzy sets (including type-1 and type-2 fuzzy sets). Some potential new research directions on fuzzy group decision making are suggested.

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Section: B Methodologies Of Group Decision Making Under Fuzzinessmentioning

confidence: 99%

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

John

Zhou²,

Garibaldi

et al. 2008

IJCIR

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“…OWA operators have been applied in different areas, such as multicriteria decision-making [43] [63], expert systems [64] and fuzzy group decision making process [65] [55].…”

Section: Ordered Weighted Averaging (Owa)mentioning

confidence: 99%

Aggregation Operators Review - Mathematical Properties and Behavioral Measures

Martínez¹,

Acosta²

2015

IJISA

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Abstract-A problem that humans must face very often is that of having to add, melt or synthesize information, that is, combine together a series of data from various sources to reach a certain conclusion or make a certain decision. This involves the use of one or more aggregation operators capable to provide a collective preference relation. These operators must be chosen according to specific criteria taking into account the characteristic properties of each operator. Some conditions to be taken into account to identify them are the following: axiomatic strength, empirical setting, adaptability, numerical efficiency, compensation and compensation range, added behavior and scale level required of the membership functions. It is possible to establish a general list of possible mathematical properties whose verification might be desirable in certain cases: boundary conditions, continuity, not decreasing monotony, symmetry, idempotence, associativity, bisymmetry, selfdistributivity, compensation, homogeneity, translativity, stability, ϕ-comparability, sensitivity and locally internal functions. For analyze the attitudinal character of the aggregation operator the following measures are studied: disjunction degree (orness), dispersion, balance and divergence. In this paper, a review of these issues is presented.Index Terms-Aggregation, aggregation operators, behavioral measures of aggregation operators, intersection operators, OWA operators.

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“…One uses real values [5][6][7], while the other uses symbols of natural languages [8,9]. According to the rules of thinking quantization explained in Section 2.2, judgment matrixes can be constructed by the experts invited [10].…”

Section: Judgment Matrixmentioning

confidence: 99%

Evaluation of Construction Scheme of Comprehensive Technical Support Center of Strategic Backside Warehouse

Deng¹,

Wang²,

Gan³

2015

Proceedings of the 2015 International Conference on Management Science and Management Innovation

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Abstract. The quantified description obtained by evaluating construction scheme of Comprehensive Technical Support Center (CTSC) of strategic backside warehouse synthetically, which can be regarded as reference for decision, is directively meaningful in reality. Combining the group analytic hierarchy process (GAHP) with fuzzy theory, the subjective weights are obtained firstly, then the changeable synthetic weights are formed by means of modifying the obtained subjective weights, and thus the comprehensive weights can be computed. Finally, according to the results of synthetic evaluation obtained by applying fuzzy synthetic evaluation method to each construction scheme, the optimum scheme can be selected. Case results demonstrate that the proposed method is beneficial to evaluate the construction scheme of CTSC.

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Linguistic labels for expressing fuzzy preference relations in fuzzy group decision making

Cited by 122 publications

References 20 publications

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

Aggregation Operators Review - Mathematical Properties and Behavioral Measures

Evaluation of Construction Scheme of Comprehensive Technical Support Center of Strategic Backside Warehouse

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