Improved Accuracy Function for Interval-Valued Intuitionistic Fuzzy Sets and Its Application to Multi–Attributes Group Decision Making

Juyal, Divya; Kumar, Sanjay

doi:10.1080/01969722.2017.1412890

Cited by 39 publications

(26 citation statements)

References 28 publications

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“…Wan et al [ 34 ] proposed new generalized aggregation operators for triangular intuitionistic fuzzy numbers and presented some applications for multi-group decision-making. Other papers in the area of decision-making which considers the use of triangular fuzzy numbers are, but not restricted to: Krohling and Pacheco [ 35 ] by providing an improved accuracy function; Wan et al [ 36 ] by extending the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method; Yuan and Luo [ 37 ] by developing a novel intuitionistic fuzzy entropy and evidential reasoning; Chen and Li [ 38 ] by considering a dynamic model; and Qin et al [ 39 ] by the extending TODIM (acronym in Portuguese for interactive and multicriteria decision-making) method.…”

Section: State-of-the-artmentioning

confidence: 99%

Linear Programming and Fuzzy Optimization to Substantiate Investment Decisions in Tangible Assets

Boloș

Bradea

Delcea

2020

Entropy

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This paper studies the problem of tangible assets acquisition within the company by proposing a new hybrid model that uses linear programming and fuzzy numbers. Regarding linear programming, two methods were implemented in the model, namely: the graphical method and the primal simplex algorithm. This hybrid model is proposed for solving investment decision problems, based on decision variables, objective function coefficients, and a matrix of constraints, all of them presented in the form of triangular fuzzy numbers. Solving the primal simplex algorithm using fuzzy numbers and coefficients, allowed the results of the linear programming problem to also be in the form of fuzzy variables. The fuzzy variables compared to the crisp variables allow the determination of optimal intervals for which the objective function has values depending on the fuzzy variables. The major advantage of this model is that the results are presented as value ranges that intervene in the decision-making process. Thus, the company’s decision makers can select any of the result values as they satisfy two basic requirements namely: minimizing/maximizing the objective function and satisfying the basic requirements regarding the constraints resulting from the company’s activity. The paper is accompanied by a practical example.

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Section: State-of-the-artmentioning

confidence: 99%

Linear Programming and Fuzzy Optimization to Substantiate Investment Decisions in Tangible Assets

Boloș

Bradea

Delcea

2020

Entropy

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“…The ranking of two numbers in fuzzy algebraic structures has a certain importance, especially in MADM problems, medical diagnostics problems, and in pattern recognition. There are several rules defined for the ranking of two numbers of a certain fuzzy algebraic structure, as discussed in Section 1 [1, 2,38]. Here, we follow the idea of Joshi and Kumar [38], to define a score function for an IVTSFN.…”

Section: Interval-valued T-spherical Fuzzy Setmentioning

confidence: 99%

“…The WA and WG operators for IVIFSs are proposed by Wang [29] and Wei and Wang [30], respectively, while some generalized WA and generalized WG operators for IVIFSs have been developed by Yu [31] and Xu and Cai [32], respectively. Some other useful tools in the aggregation of IFSs and IVIFSs, along with their applications, were also constructed [33][34][35][36][37][38]. For PyFSs, Rahman et al [39] and Peng and Yang [40] developed the Pythagorean fuzzy WA and WG aggregation operators.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Investment Policy Based on Multi-Attribute Decision-Making Using Interval Valued T-Spherical Fuzzy Aggregation Operators

et al. 2019

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Expressing the measure of uncertainty, in terms of an interval instead of a crisp number, provides improved results in fuzzy mathematics. Several such concepts are established, including the interval-valued fuzzy set, the interval-valued intuitionistic fuzzy set, and the interval-valued picture fuzzy set. The goal of this article is to enhance the T-spherical fuzzy set (TSFS) by introducing the interval-valued TSFS (IVTSFS), which describes the uncertainty measure in terms of the membership, abstinence, non-membership, and the refusal degree. The novelty of the IVTSFS over the pre-existing fuzzy structures is analyzed. The basic operations are proposed for IVTSFSs and their properties are investigated. Two aggregation operators for IVTSFSs are developed, including weighted averaging and weighted geometric operators, and their validity is examined using the induction method. Several consequences of new operators, along with their comparative studies, are elaborated. A multi-attribute decision-making method in the context of IVTSFSs is developed, followed by a brief numerical example where the selection of the best policy, among a list of investment policies of a multinational company, is to be evaluated. The advantages of using the framework of IVTSFSs are described theoretically and numerically, hence showing the limitations of pre-existing aggregation operators.

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“…This was used to multi-attribute decision making with incomplete attribute weight information. Krohling and Pacheco [16] used an improved accuracy function for interval-valued intuitionistic fuzzy sets and applied it to multi-attribute group decision making. Joshi and Kumar [17] developed a fuzzy analytic hierarchy process method for tackling the uncertainty and imprecision associated with MCDM problems.…”

Section: State Of the Artmentioning

confidence: 99%

A Fuzzy Logic Algorithm for Optimizing the Investment Decisions within Companies

2019

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As companies operate in a competitive environment, where the struggle for survival on the market is rather tough, the top management face new challenges to identify methods, and even techniques, which allows it to select from the market those assets that provide an optimal ratio between the acquisition cost and the economic performance. In this context, a fuzzy logic managerial decision tool for the assets acquisition is proposed with the paper. The algorithm has three main components: the matrix of the membership degree of the existing bids to asset selection criteria, using fuzzy triangular numbers; the vector of the global membership degree of the bids to the selection criteria and the maximum of the global membership degree as an inference operator for establishing the validated bids by the algorithm. Two scenarios of asset acquisition were tested. After simulations, it was determined that the proposed fuzzy logic managerial decision tool combines, with very good results, the acquisition cost of the assets with their economic performance.

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Improved Accuracy Function for Interval-Valued Intuitionistic Fuzzy Sets and Its Application to Multi–Attributes Group Decision Making

Cited by 39 publications

References 28 publications

Linear Programming and Fuzzy Optimization to Substantiate Investment Decisions in Tangible Assets

Linear Programming and Fuzzy Optimization to Substantiate Investment Decisions in Tangible Assets

Evaluation of Investment Policy Based on Multi-Attribute Decision-Making Using Interval Valued T-Spherical Fuzzy Aggregation Operators

A Fuzzy Logic Algorithm for Optimizing the Investment Decisions within Companies

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