An interval type-2 fuzzy extension of the TOPSIS method using alpha cuts

Dymova, Ludmila; Sevastjanov, Pavel; Tikhonenko, Anna

doi:10.1016/j.knosys.2015.03.014

Cited by 74 publications

(45 citation statements)

References 63 publications

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“…It is based on defuzzification of interval type-2 fuzzy sets using concept of ranking values. D y m o v a, S e v a s t j a n o v and T i k h o n e n k o [8] propose a new method for fuzzy MCDM based on the α-cuts of IT2 fuzzy values to avoid the restrictions concerned with the different shapes.…”

Section: Short Literature Reviewmentioning

confidence: 99%

TOPSIS Modification with Interval Type-2 Fuzzy Numbers

Ilieva

2016

Cybernetics and Information Technologies

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Section: Short Literature Reviewmentioning

confidence: 99%

TOPSIS Modification with Interval Type-2 Fuzzy Numbers

Ilieva

2016

Cybernetics and Information Technologies

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“…For details on the application of interval type-2 fuzzy numbers in decision-making, see Ghorabaee (2016) and Demova et al (2015). For the applications of intuitionistic fuzzy sets, see Garg (2016aGarg ( , b, c, 2017a and Singh and Garg (2017), and for neutrosophic sets, see Nancy and Garg (2016).…”

Section: Introductionmentioning

confidence: 99%

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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“…al. [4], proposed an IT2 fuzzy TOPSIS method using alpha cuts, and Kilic and Kaya [8], proposed an IT2 fuzzy TOPSIS method to evaluate an investment project producing a final ranking.…”

Section: Introductionmentioning

confidence: 99%

Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS

Madi

Garibaldi

Wagner

2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-Multi-criteria decision making (MCDM) problems are a well known category of decision making problem that has received much attention in the literature, with a key approach being the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). While TOPSIS has been developed towards the use of Type-2 Fuzzy Sets (T2FS), to date, the additional information provided by T2FSs in TOPSIS has been largely ignored since the final output, the Closeness Coefficient (CC), has remained a crisp value. In this paper, we develop an alternative approach to T2 fuzzy TOPSIS, where the final CC values adopt an interval-valued form. We show in a series of systematically designed experiments, how increasing uncertainty in the T2 membership functions affects the interval-valued CC outputs. Specifically, we highlight the complex behaviour in terms of the relationship of the uncertainty levels and the outputs, including non-symmetric and non-linear growth in the CC intervals in response to linearly growing levels of uncertainty. As the first TOPSIS approach which provides an interval-valued output to capture output uncertainty, the proposed method is designed to reduce the loss of information and to maximize the benefit of using T2FSs. The initial results indicate substantial potential in the further development and exploration of the proposed and similar approaches and the paper highlights promising next steps.

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An interval type-2 fuzzy extension of the TOPSIS method using alpha cuts

Cited by 74 publications

References 63 publications

TOPSIS Modification with Interval Type-2 Fuzzy Numbers

TOPSIS Modification with Interval Type-2 Fuzzy Numbers

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

Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS

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