Multifactorial decision making based on type-2 fuzzy sets and factor space approach

Multifactorial condition assessment for power transformers

“…In the description frame (U, C, F], if the extension of α is a type-1 fuzzy set A, α ∈ C and A ∈ F (U) (F (U) represents all fuzzy sets on domain U), which can be expressed as [30] A:…”

“…where μ A (u) is called the membership grade of u with respect to α or A. For any factor f, according to Zadeh's extension principle, f can be extended as follows [30]:…”

“…When the factor state f is used to characterise the described object u, the factor state of u can be represented as follows [30]:…”

“…an ordinary fuzzy set) in which the membership function falls into a fuzzy set on the interval [0, 1] [26,27]. The factor space theory, which were initially introduced by Wang and Li [28,29], provides a mathematical framework to solve the structure and selection of a domain and variable [30]. The factor space theory has been successfully applied to decision-making, which is used to evaluate objects in a fuzzy decision-making environment with multiple factors and provide explanations for decision results [31].…”

Application of Type-2 Fuzzy AHP-ARAS for Selecting Optimal WEDM Parameters

“…The amalgamation of T2FS with the MCDM method is becoming a major flare in decision making. Zhou et al (2016) [29] applied a novel approach by coupling the T2FS and factor space approach with MCDM. According to Sukhveer Singh and Harish Garg (2016) [30], the decision-makers encounter a problem regarding preferences of objectives.…”