Ranking fuzzy numbers by preference ratio

Modarres, Mohammad; Sadi‐Nezhad, Soheil

doi:10.1016/s0165-0114(98)00427-8

Cited by 114 publications

(48 citation statements)

References 5 publications

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“…This method, which compares fuzzy numbers relatively, was proposed by Modarres and Sadi-Nezhad (2001). In this method, fuzzy numbers are evaluated point by point and ranked at each point.…”

Section: Introduction To Fuzzy Ranking By Preference Ratio Methodsmentioning

confidence: 99%

“…Some studies (Abbasbandy & Asadi, 2006;Huijun & Jianjun, 2006;Wang et al, 2006;Asady & Zendehnam, 2007;Asady, 2010) introduced a ranking function to map fuzzy numbers to real numbers and then applied usual ranking methods. Other researchers (Delgado et al, 1988;Mabuchi, 1988;Tseng & Klein, 1989;Modarres & Sadi-Nezhad, 2001) defined a comparison function that maps two fuzzy numbers to a real number which denotes the domination degree of one fuzzy number to the other one.…”

Section: Introductionmentioning

confidence: 99%

“…Section 2 deals with an introduction to ranking fuzzy numbers by "Preference Ratio Method" (Modarres & Sadi-Nezhad, 2001) and discussion on the weakness of this method. A novel comparison method based on utility function is presented in Section 3.…”

Section: Introductionmentioning

confidence: 99%

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Ranking fuzzy numbers using preference ratio: A utility function approach

Sadi‐Nezhad¹,

Shahnazari-Shahrezaei²

2013

10.5267/j.dsl

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Ranking fuzzy numbers is one of the most important phases in any decision making process in fuzzy environments. In most cases, we deal with fuzzy numbers in the field of evaluating alternatives with fuzzy information or linguistic variables. This paper investigates ranking fuzzy numbers using the concept of preference ratio, introduces the weakness of this method, and proposes a new approach that overcomes the shortcoming of existing method. The proposed approach which is based on the concept of utility function takes the opinion of DM for ranking fuzzy numbers into account.

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“…This method, which compares fuzzy numbers relatively, was proposed by Modarres and Sadi-Nezhad (2001). In this method, fuzzy numbers are evaluated point by point and ranked at each point.…”

Section: Introduction To Fuzzy Ranking By Preference Ratio Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ranking fuzzy numbers using preference ratio: A utility function approach

Sadi‐Nezhad¹,

Shahnazari-Shahrezaei²

2013

10.5267/j.dsl

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“…Step 6: Because TFN's were used to represent the vagueness in the judgement matrix, the FCR values obtained from Equation 17 were in the form of a set with 3 values. The FCR was determined as a preference ratio, which according to [39], is defined as the percentage of the ith fuzzy number within a set being the most preferred one. This ratio is expressed by Equation 18 as follows.…”

Section: Calculating the Fuzzy Consistency Ratiomentioning

confidence: 99%

Integrating Geographical Information Systems, Fuzzy Logic and Analytical Hierarchy Process in Modelling Optimum Sites for Locating Water Reservoirs. A Case Study of the Debub District in Eritrea

Tsiko

Haile

2011

Water

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Abstract:The aim of this study was to model water reservoir site selection for a real world application in the administrative district of Debub, Eritrea. This is a region were scarcity of water is a fundamental problem. Erratic rainfall, drought and unfavourable hydro-geological characteristics exacerbates the region's water supply. Consequently, the population of Debub is facing severe water shortages and building reservoirs has been promoted as a possible solution to meet the future demand of water supply. This was the most powerful motivation to identify candidate sites for locating water reservoirs. A number of conflicting qualitative and quantitative criteria exist for evaluating alternative sites. Decisions regarding criteria are often accompanied by ambiguities and vagueness. This makes fuzzy logic a more natural approach to this kind of Multi-criteria Decision Analysis (MCDA) problems. This paper proposes a combined two-stage MCDA methodology. The first stage involved utilizing the most simplistic type of data aggregation techniques known as Boolean Intersection or logical AND to identify areas restricted by environmental and hydrological constraints and therefore excluded from further study. The second stage involved integrating fuzzy logic with the Analytic Hierarchy Process (AHP) to identify optimum and back-up candidate water reservoir sites in the area designated for further study. OPEN ACCESSWater 2011, 3 255

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“…Several ranking methods have been proposed so far, by Cheng (1998), Modarres and Sadi-Nezhad (2001) and Nojavan and Ghazanfari (2006). In this paper we use another ranking system for canonical fuzzy numbers which is very realistic and is defined by Yao and Wu as the following: …”

Section: Yao-wu Signed Distancementioning

confidence: 99%

Bootstrap testing fuzzy hypotheses and observations on fuzzy statistic

Akbari¹,

Rezaei²

2010

Expert Systems with Applications

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a b s t r a c tThe bootstrap is a simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, testing statistical hypotheses, and so forth, in almost any nonparametric estimation problem. A new approach for bootstrap testing fuzzy hypotheses based on fuzzy test statistic is introduced. In this paper we describe bootstrap method that is designed directly for hypothesis testing for fuzzy data based on Yao-Wu signed distance.

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Ranking fuzzy numbers by preference ratio

Cited by 114 publications

References 5 publications

Ranking fuzzy numbers using preference ratio: A utility function approach

Ranking fuzzy numbers using preference ratio: A utility function approach

Integrating Geographical Information Systems, Fuzzy Logic and Analytical Hierarchy Process in Modelling Optimum Sites for Locating Water Reservoirs. A Case Study of the Debub District in Eritrea

Bootstrap testing fuzzy hypotheses and observations on fuzzy statistic

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