A correlation based Intuitionistic fuzzy TOPSIS method on supplier selection problem

Solanki, Rinki; Gulati, Gabriel; Tiwari, Ashutosh; Lohani, Q. M. Danish

doi:10.1109/fuzz-ieee.2016.7737951

Cited by 18 publications

(10 citation statements)

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“…The positive and negative separation measures and relative closeness (RC) coefficient values of Table 8 can be compared with their probabilistic counterparts by the help of Table 7. The adaptive proposed PI-TOPSIS algorithm delivers that ranking which exact matches with the rankings obtained from other standard methods (see [23], [11]). On the other hand ranking obtained by non-adaptive IF-TOPSIS method differs with those of [23] and [11].…”

Section: B Evaluation Of Alternatives By Proposed Probabilistic Intuitionistic Fuzzy Topsis Algorithmmentioning

confidence: 52%

“…Therefore, IFS is a generalized framework of modeling fuzziness. Moreover, the theory of intuitionistic fuzzy sets is widely applied in numerous fields such as modeling imprecision [5], pattern recognition [6], computational intelligence [7], medical diagnosis ( [8], [9]), decision making ( [10], [11]) and face identification [12].…”

Section: Introductionmentioning

confidence: 99%

“…The alternatives given in the problem are ranked by the ranking procedure of MCDM method and then best alternative is chosen from them. Some of the well-known ranking methods are technique for order preference by similarity to ideal solutions (TOPSIS) ( [15], [11]), weighted sum model (WSM) [16], analytic hierarchy process (AHP) [17], elimination and choice translating reality (ELECTRE) [18], Grey relational analysis (GRA) [19] etc. Here every method is implemented in three steps as follows:…”

Section: Introductionmentioning

confidence: 99%

“…The uncertainty and impreciseness is observed in the problem because experts have different perception about significance levels of the criteria in the decision making or decisions are taken within a time constraint with limited information processing capacities, and lack of data knowledge [22]. Such situations are predominantly handled by fuzzy approach based MCDM methods [11].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Intuitionistic Fuzzy Decision Making Algorithms

2021

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In this paper, the probabilistic version of intuitionistic fuzzy decision making methods is introduced to show the duality present within probabilistic distance. Here, two types of methods, namely, probabilistic intuitionistic fuzzy TOPSIS (PI-TOPSIS) algorithm and probabilistic intuitionistic fuzzy face identification (PIFI) algorithm are proposed. The PI-TOPSIS is utilizing probabilistic distance as the separation measure; for its justification rankings and reduced information loss of the multi criteria decision making problems are compared. Our other proposed decision making method called PIFI algorithm handles Face Identification Problem. It is exemplified by better benchmark indexes of PIFI in comparison to support vector machine, naive bais classifier, fuzzy support vector machine algorithms, that, the probabilistic distance can be used as a similarity measure. The two well-known feature extraction techniques, called Local binary pattern (LBP) and Angular radial transformation (ART) are employed to extract the features in the face images. Further, it is concluded from the experimental findings, that, the proposed algorithms are adaptive in nature.

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Section: B Evaluation Of Alternatives By Proposed Probabilistic Intuitionistic Fuzzy Topsis Algorithmmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic Intuitionistic Fuzzy Decision Making Algorithms

2021

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“…Therefore, it is difficult to encounter problems of uncertain or incomplete data. There are several authors who have proposed MCDM methods using fuzzy set theory or intuitionistic fuzzy set for the supplier selection (Boran et al, 2009;Kavita et al, 2009;Yayla, 2012;Maldonado-Macías et al, 2014;Pérez et al, 2015;Omorogbe, 2016;Solanki et al, 2016;Zeng and Xiao, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Criteria Decision Making Method for Evaluating Water Reuse Applications under Uncertainty

Nguyen

2019

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There are currently many places in the world where water is scarce. Therefore, water reuse has been mentioned by many researchers. Evaluation of water reuse applications is the selection of the best water reuse application of the existing options; it is also one of the applications of multi-criteria decision making (MCDM). In this paper, we introduce a new dissimilarity measure of picture fuzzy sets. This new measure overcomes the restriction of other existing dissimilarity measures of picture fuzzy sets. Then, we apply it to the multi-criteria decision making. Finally, we refer to a new method for selecting the best water reuse application of the available options by using the picture fuzzy MCDM.

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