A novel approach to multi‐attribute group decision making based on power neutrality aggregation operator for
            <i>q</i>
            ‐rung orthopair fuzzy sets

Fuzzy entropy concerning a fuzzy set computes the imprecision content of that set whereas the content of precision is evaluated by using the notion of fuzzy knowledge measure. In this paper, we suggest a twoparametric version of the knowledge measure in fuzzy settings. Further, we investigate its novelty and advantages from various viewpoints, such as attribute weight computation, ambiguity computation, and appropriate handling of the structured linguistic variables. We also introduce a two-parametric generalized fuzzy accuracy measure (GFAM) and demonstrate its application in pattern analysis. Additionally, we contrast the effectiveness of the suggested fuzzy accuracy measure with several existing compatibility measures. We also consider the Multiobjective Optimization on the basis of Ratio Analysis (MOORA) method of multiple attribute decision-making (MADM) based on the proposed two-parametric generalized fuzzy knowledge measure and two-parametric GFAM. We also contrast the MADM method with the conventional MOORA method.

Section: Discussionmentioning

confidence: 97%

Two‐parametric generalized fuzzy knowledge measure and accuracy measure with applications

Singh

Ganie

2021

“…Example Multiattribute decision‐making 5,67–71 is a prevalent tool in the decision‐making process. The demand of face mask has increased enormously due to the recent outbreak of the novel COVID‐19 virus.…”

Section: Numerical Study and Applicationsmentioning

confidence: 99%

Construction and generation of distance and similarity measures for intuitionistic fuzzy sets and various applications

Gohain

Dutta

Gogoi³

et al. 2021

The distance measure between intuitionistic fuzzy sets (IFSs) is a concept of very contemporary interest among the researchers in the field of decision-makings, such as pattern recognition, medical diagnosis, and multiattribute decision-making (MADM) problems. Consequently, diverse distance measures are developed and used in determining the similarity and dissimilarity between IFSs. In the existing methods, the distance measures are calculated based on the geometry of the IFSs. However, the IFSs hold information about the elements in a set. As such, some of the existing distance measures are misleading and unreasonable.Hence, in this paper, a nonlinear distance formula is devised to follow the problem definition. Further, by explicitly proving the distance properties, it is being established that the distance formula is a distance measure. Further, theories for the construction of distance measures are developed. The convex combination of two distance measures is also a distance measure is being proved explicitly. Furthermore, based on the proposed distance measures, similarity measures have been developed. Aside from that, an intriguing idea has been introduced, namely, that an infinite number of distance measures can be constructed from a given pair of distance measures. whenwhich is a disadvantage of their method. As a result, the identical property is not applicable. Furthermore, this property is also not satisfied by the work of Xiao. 7 Apart, from these, a few methods, namely, by Szmidt and Kacprzyk, 32 Luo et al., 50 Luo and Zhao, 40 and Song et al. 45 do not satisfy the essential distance properties.This motivated the development of new distance measures to overcome the limitations and drawbacks of existing methods. As a result, a new distance measure has been developed, and the distance properties have been explicitly established. It is seen that the cross-evaluation factor used by Ngan et al. 13 is a significant parameter. They used the difference of maximum of the crossevaluation factor ignoring the difference of minimum of the cross-evaluation factor. As such, some limitations are observed in their measure. As such, in this study both the minimum and maximum of the difference of the cross-evaluation factor have been used to compensate for their limitations. In addition, the theory has been developed to generate newer distance measures from two existing distance measures. Also, this concept can be used to develop newer distance measures. Furthermore, it has been demonstrated that the convex combination of two distance measures is also a distance measure. As a result, it is established that an infinite number of distance measures can be constructed from two distance measures. In addition, the distance measure is used to generate new similarity measures. Furthermore, these measures are applied to decision-making problems, pattern recognition, and medical diagnosis problems.The outline of the paper is as follows. In Section 2, preliminary definitions and some existing distance measures are discussed. In S...

“…As future work, this study will focus on applying different fuzzy MCDM methods to this problem (like, Fuzzy TOPSIS, Fuzzy Promethee, and Fuzzy Multimoora 39 ) to compare their performances to the traditional MCDM techniques that have been used with the optimization algorithm. We also propose considering different decision matrices obtained from several decision‐makers with different strategic views of the problem to create a single decision matrix 40 and analyze the influence of different aggregation operators and techniques, such as the q ‐rung operator, among others.…”

Section: Final Remarksmentioning

confidence: 99%

An integrated multicriteria decision‐making approach for distribution system expansion planning

Mussoi

Teive

2021

The project selection problem in an electric distribution utility, which is part of the network expansion planning task, requires a formal process of prioritizing some projects since the budget is limited. A complete solution to this problem, involving optimization and a decision‐making model, is still a few explored in the literature. We proposed an integrated multicriteria decision‐making methodology, including optimization and solutions ranking. The optimization module finds a set of Pareto‐optimal portfolios, using the multi‐objective genetic algorithm nondominated sorting genetic algorithm‐II to select and schedule the reinforcement projects in a multistage planning horizon. The proposed decision support module ranks the project portfolios based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Simple Multi‐Attribute Rating Technique (SMART), considering the decision‐maker profile embedded into the model by the Rank‐Order Centroid (ROC) and Analytic Hierarchy Process (AHP) criteria weights. This paper tackles this methodology's theoretical foundations, taking into account some essential attributes, such as power quality and operational performance indexes, the number of consumers, and projects' financial impacts. We presented realistic case studies to show how portfolios are composed, not only when all nine attributes of projects are included in the optimization module, but also when the criteria weights are changed in the decision support module. This paper's main contribution lies in proposing a unique model to find optimal expansion plans and rank these project portfolios, considering the decision‐maker profile adherent to its goals. About the decision‐making methods applied in this problem, we infer that the TOPSIS performance overcomes SMART in all analyzed problems and the ROC and AHP weights indicate the same alternative in most of the analyzed cases.