A new parametric method for ranking fuzzy numbers

Shureshjani, Roohollah Abbasi; Darehmiraki, Majid

doi:10.1016/j.indag.2013.02.002

Cited by 38 publications

(29 citation statements)

References 25 publications

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“…It gives the same degree of importance to x and y in ranking, while actually, they have different degrees of importance since the importance of x is higher than y (Wang and Lee 2008). It was also pointed out that ranking order differs with the decision level (Liou and Wang 1992;Shureshjani and Darehmiraki 2013). Therefore, the value x c of the center of the final ellipse is used for ranking, while the value of y is used as the decision level at which the ranking is done.…”

Section: Fuzzy Numbers With Linear Inequalitiesmentioning

confidence: 99%

“…Since fuzzy numbers are represented by possibility distributions, they may overlap with each other. Thus, to decide whether one fuzzy number is bigger than or smaller than the other is difficult (Shureshjani and Darehmiraki 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Many existing methods for ranking fuzzy numbers are neutral; they do not reflect the decision maker's subjective attitude in the process of ranking. To overcome this limitation, Shureshjani and Darehmiraki (2013) introduced an approach for ranking fuzzy numbers based on a-cuts representing the decision maker's preference information explicitly. Since almost each method has a certain drawback, still none of the existing ranking methods is superior to the others (Sharma 2015).…”

Section: Introductionmentioning

confidence: 99%

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A new approach for Robot selection in manufacturing using the ellipsoid algorithm

Sharaf

2017

J Ind Eng Int

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The choice of suitable robots in manufacturing, to improve product quality and to increase productivity, is a complicated decision due to the increase in robot manufacturers and configurations. In this article, a novel approach is proposed to choose among alternatives, differently assessed by decision makers on different criteria, to make the final evaluation for decision-making. The approach is based on the ellipsoid algorithm for systems of linear inequalities. Most of the ranking methods depend on integration that becomes complicated for nonlinear membership functions, which is the case in robot selection. The method simply uses the membership function or its derivative. It takes the decision maker's attitude in ranking. It effectively ranks fuzzy numbers and their images, preserving symmetry. It is a simple recursive algebraic formula that can be easily programmed. The performance of the algorithm is compared with the performance of some existing methods through several numerical examples to illustrate its advantages in ranking, and a robot selection problem is solved.

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Section: Fuzzy Numbers With Linear Inequalitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new approach for Robot selection in manufacturing using the ellipsoid algorithm

Sharaf

2017

J Ind Eng Int

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“…Some researchers employed a distance for ordering of fuzzy numbers such as Abbasbandy and Asady [1], Yao and Wu [25], Allahviranloo and Adabitabar Firozja [4], Deng [21] and Janizade-Haji et al [14]. Some researchers as [2,15,16] presented a defuzzification method for ranking fuzzy numbers. Vincent and Luu in [22] proposed improve their ranking method for fuzzy numbers with integral values.…”

Section: Introductionmentioning

confidence: 99%

Vague ranking of fuzzy numbers

2017

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In a lot of scientific models in the real world, we confront with comparing fuzzy numbers as decision-making procedures and etc. It will be interest, if we know that, comparison discuss is sometimes ambiguous. Hence, this article focus on ranking fuzzy numbers with protection ambiguity. Our idea for this work is based on this claim that ranking of two fuzzy numbers should be a vague value. However, we utilize the notion of max and min fuzzy simultaneously.

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“…The comparison of fuzzy parameters needed the ranking methods for their quantitative comparison. Several methods have been developed for ranking of fuzzy numbers and are available in literature given by Cheng [9], Yao and Wu [10], Yao and Lin [27], Tran and Duckstein [11], Chu, Ta-Chun, Tsao, C. Tsen [12], Thus Mitchell [7], Grzegorzewski [13], Abbasbandy and Asadi [14], Shureshjani and Darehmiraki [15]. Several methods were also developed to find solution of fuzzy linear programming problems using ranking methods.…”

Section: Introductionmentioning

confidence: 99%

A Note on Solving a Fully Intuitionistic Fuzzy Linear Programming Problem based on Sign Distance

Bharati¹,

Singh²

2015

IJCA

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The paper presents a new method to find the optimal solution of a fully intuitionistic fuzzy linear programming (FIFLP) problem. It uses the sign distance between intuitionistic fuzzy numbers for their comparison. The proposed methods have been applied for solving a FIFLP problem with equality constraints. The proposed method is convenient for implementation to solution of FIFLP problems arising in real life situations. General TermsLinear programming, distance function. KeywordsIntuitionistic fuzzy sets, Triangular intuitionistic fuzzy numbers, Sign distance between intuitionistic fuzzy numbers, Fully intuitionistic fuzzy linear programming problem.

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A new parametric method for ranking fuzzy numbers

Cited by 38 publications

References 25 publications

A new approach for Robot selection in manufacturing using the ellipsoid algorithm

A new approach for Robot selection in manufacturing using the ellipsoid algorithm

Vague ranking of fuzzy numbers

A Note on Solving a Fully Intuitionistic Fuzzy Linear Programming Problem based on Sign Distance

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