On fuzzy rank-ordering in polyoptimization

Wagenknecht, Michael; Hartmann, Klaus

doi:10.1016/s0165-0114(83)80084-0

Cited by 48 publications

(14 citation statements)

References 5 publications

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“…this paper, we have presented a validation procedure based on clustering [7,[17][18][19] for validating inconsistent ranking results for a given problem data set. An empirical study of major ports of India to indicate performance evaluation problem has been conducted to illustrate how the procedure can be used to help select the most valid ranking result for a given problem.…”

Section: Resultsmentioning

confidence: 99%

“…Numerous studies have since been conducted on the development of fuzzy MA methods [3][4][5][6][7] and their applications to various fuzzy MA decision problems [7][8][9][10][11]. However, the large number of available fuzzy MA methods may confuse the decision maker (DM) who is new to MA methodology, as selecting the right methods for solving a particular problem has become another MA problem [12].…”

Section: Introductionmentioning

confidence: 99%

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Validating AHP, fuzzy alpha cut and fuzzy preference programming method using clustering technique

Mitra

2010

OPSEARCH

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Fuzzy multicriteria analysis (MA) methods suitable for a given decision problem usually differ in aggregation processes for handling the alternatives' performance ratings and criteria weights. Due to their structural differences, these methods often produce inconsistent ranking results for the same fuzzy MA problem. This paper presents a validation procedure using fuzzy clustering for selecting among inconsistent ranking results produced by analytical hierarchy process (AHP), fuzzy alpha cut and fuzzy preference programming (FPP) methods for a given problem. The procedure compares the ranking results obtained by fuzzy MA methods using different aggregation algorithms with the clustering results of the alternatives by fuzzy clustering. An empirical study of evaluating major port trusts of India is conducted to demonstrate the effectiveness of the validation procedure.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validating AHP, fuzzy alpha cut and fuzzy preference programming method using clustering technique

Mitra

2010

OPSEARCH

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“…Although the concepts of fuzzy sets have been widely incorporated in the AHP to address the problem of subjective uncertainty (van Laarhoven and Pedrycz, 1983;Wagenknecht and Hartmann, 1983;Buckley, 1985;Chang, 1996), few papers have extended the ANP to cope with the uncertain judgments. In Mikhailov and Singh's (2003) paper, the crisp local weights are first derived from the fuzzy pairwise judgments by using the fuzzy preference programming (FPP) method (Mikhailov, 2003) and then the crisp weighted supermatrix is raised to a steady-state to obtain the final priority vectors.…”

Section: Introductionmentioning

confidence: 99%

A mathematical programming model for the fuzzy analytic network process—applications of international investment

Huang

2012

Journal of the Operational Research Society

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Currently, the analytic network process (ANP) method is widely employed to consider the multiple criteria analysis problems with dependence and feedback effects. However, in order to extend the ANP to resolve the problem of uncertainty or human subjective judgment, the concepts of fuzzy numbers should be incorporated into the ANP to represent the subjective uncertain pairwise judgments. In this paper, therefore, we propose a novel fuzzy analytic network process (FANP) model by solving a mathematical programming problem. Unlike other FANPs, the proposed method does not require the reciprocity assumption of the weight ratios between criteria, and it can derive local and global weights simultaneously in a single model. Two numerical examples of international investment problems are used to demonstrate the proposed method.

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“…Several authors presented methods for the case where the pairwise fuzzy comparisons are fuzzy numbers [2][3][4][5][6]. We will argue that all these methods have drawbacks, and present a new, simple method which does not suffer from these drawbacks.…”

Section: Introductionmentioning

confidence: 98%

“…Now consider the case where the matrix elements A ij for i/=j are fuzzy numbers. Buckley [2] and Wagenknecht and Hartmann [3] generalised method b, and Van Laarhoven and Pedrycz [4], De Boender, De Graan and Lootsma [5] and Mikhailov [6] generalized method d.…”

Section: Introductionmentioning

confidence: 99%

Rankings from Fuzzy Pairwise Comparisons

Broek

Noppen

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

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We propose a new method for deriving rankings from fuzzy pairwise comparisons. It is based on the observation that quantification of the uncertainty of the pairwise comparisons should be used to obtain a better crisp ranking, instead of a fuzzified version of the ranking obtained from crisp pairwise comparisons. With our method, a crisp ranking is obtained by solving a linear programming problem, when the fuzzy pairwise comparisons are fuzzy triangular numbers. Our method simplifies the recent method by Mikhailov.

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On fuzzy rank-ordering in polyoptimization

Cited by 48 publications

References 5 publications

Validating AHP, fuzzy alpha cut and fuzzy preference programming method using clustering technique

Validating AHP, fuzzy alpha cut and fuzzy preference programming method using clustering technique

A mathematical programming model for the fuzzy analytic network process—applications of international investment

Rankings from Fuzzy Pairwise Comparisons

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