An integrated interval type-2 fuzzy technique for democratic–autocratic​ multi-criteria decision making

Wan, Shouhong; Chen, Zehui; Dong, Jiuying

doi:10.1016/j.knosys.2020.106735

Cited by 44 publications

(53 citation statements)

References 56 publications

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“…As a result, decision-makers typically prefer to quantify subjective criteria using interval values [76]. Some researchers [15,16,[76][77][78][79][80][81] have extended the original BWM into the fuzzy environment to deal with such ambiguity in human judgement. For example, Mou et al [74] developed the traditional BWM into a group decision-based intuitionistic fuzzy multiplicative BWM (IFMBWM).…”

Section: Best-worst Methods (Bwm)mentioning

confidence: 99%

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An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS

Roy

Shaw

2021

Complex Intell. Syst.

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Mobile technology has revolutionised various business processes. Banking is one of them. Traditional banking operations are gradually changing with the introduction of efficient mobile technologies. Mobile banking (m-banking) has recently emerged as an innovative banking channel that provides continuous real-time customer service. It is expected that the market for m-banking will expand in the near future. There are currently various types of m-banking applications in the market. However, ranking and selecting efficient applications is difficult due to the involvement of multiple factors. As of now, very few studies have reported the m-banking application selection framework, left scope for further research. The current study proposes an m-banking application selection model based on a combined fuzzy best–worst method (fuzzy-BWM) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy-TOPSIS). The research was carried out in several stages, beginning with the identification of potential factors and progressing to pair-wise comparisons and the final ranking of the applications. The fuzzy set theory was applied to handle the ambiguity of the decision maker. In the first stage, fuzzy-BWM was used to determine the weight of the factors. Further, fuzzy-TOPSIS was applied to rank the m-banking applications. The present study has adopted a new fuzzy BWM, which differs significantly from the existing fuzzy-BWM, to solve the nonlinearity problem of optimisation. The applicability of the proposed model has been demonstrated through a real-life case study. The efficacy of the model has been further examined by performing a sensitivity analysis. The study observed application functionality, convenience, and performance expectancy as significant factors in selecting an m-banking application, followed by performance quality, security, and compatibility. The proposed model can assist financial institutions and customers to overcome the challenges of choosing an appropriate m-banking application. The proposed model can be used to benchmark the m-banking applications in the market.

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Section: Best-worst Methods (Bwm)mentioning

confidence: 99%

“…The study found that GITrF BWM improves overall process consistency. Wan et al [81] used trapezoidal interval type-2 fuzzy sets to extend the original BWM into a fuzzy environment. The technique can be used in a variety of situations.…”

Section: Best-worst Methods (Bwm)mentioning

confidence: 99%

An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS

Roy

Shaw

2021

Complex Intell. Syst.

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show abstract

“…Without losing the generality, it is a fuzzy set of elements that allow their members different degrees of membership in the range [0, 1]. e degree of similarity of each input variable in such a fuzzy set is thus given by the membership function [36,37]. A type-1 fuzzy membership function is defined by accurate and sharp values in the range [0, 1], while type-2 fuzzy membership function can be designed for each input variable in domain x.…”

Section: Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Janková

Dostál

2021

Mathematical Problems in Engineering

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Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

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“…Fuzzy numbers have been studied extensively. [30][31][32][33][34][35][36] TFAHP (triangular fuzzy analytic hierarchy process) is a combination of fuzzy theory and AHP. It fully considers the subjective judgment of the evaluator, the fuzziness of decision, and the preference, which makes the decision result more objective and reasonable.…”

Section: Determination Of Weight Based On Triangular Fuzzymentioning

confidence: 99%

Research on Importance Evaluation of Complex Product Parts Based on Multilayer Complex Network

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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The identification and evaluation of important parts play an important role in effective production arrangement and shortening the product development in the product design stage. In this paper, a complex product node importance evaluation method based on multilayer network is proposed to identify and evaluate importance parts faster. First, a complex product design expression network based on “function behavior structure (FBS)” multilayer complex network is established. Second, the evaluation index system of important design parts for complex products based on multilayer network is constructed. Third, a three-parameter grey relational model based on the fuzzy analytic hierarchy process and the Gini coefficient method is proposed. Finally, this method is available and feasible through taking the large permanent magnet synchronous centrifugal unit as an example.

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An integrated interval type-2 fuzzy technique for democratic–autocratic multi-criteria decision making

Cited by 44 publications

References 56 publications

An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS

An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Research on Importance Evaluation of Complex Product Parts Based on Multilayer Complex Network

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An integrated interval type-2 fuzzy technique for democratic–autocratic​ multi-criteria decision making

Cited by 44 publications

References 56 publications

An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS

An integrated fuzzy model for evaluation and selection of mobile banking (m-banking) applications using new fuzzy-BWM and fuzzy-TOPSIS

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Research on Importance Evaluation of Complex Product Parts Based on Multilayer Complex Network

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An integrated interval type-2 fuzzy technique for democratic–autocratic multi-criteria decision making