An MADM-based fuzzy parameterized framework for solar panels evaluation in a fuzzy hypersoft expert set environment

Ihsan M., Saeed M., Rahman A. U., KAMACI H., Shah N. A., Weera W.

AIMS Mathematics, vol.8, no.2, pp.3403-3427, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 2
  • Publication Date: 2023
  • Doi Number: 10.3934/math.2023175
  • Journal Name: AIMS Mathematics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Directory of Open Access Journals
  • Page Numbers: pp.3403-3427
  • Keywords: fuzzy set, soft set, hypersoft set, hypersoft expert set, fuzzy parameterized fuzzy hypersoft expert set, decision making
  • Yozgat Bozok University Affiliated: Yes


© 2023 the Author(s), licensee AIMS Press.The selection of parameters plays a vital role in the multi-attribute decision-making process. In some situations, it is observed that the nature of parameters is ambiguous and a multi-decisive opinion is necessary for managing such parametric uncertainty. In the literature, there is no suitable model that can cope with such situations. This study was purposed to develop a novel context called the fuzzy parameterized fuzzy hypersoft expert set (FPFHSE-set), which is capable of managing the uncertain nature of parameters and the multi-decisive opinion of experts collectively in one model. In this way, the proposed model may be described as the generalization of the existing model fuzzy parameterized fuzzy soft expert set (FPFSE-set). Theoretic, axiomatic and algorithmic approaches have been employed for the characterization of the basic notions of the FPFHSE-set. In order to handle multi-attribute decision-making, two algorithms are proposed and then validated by applying them to some real-world scenarios in the FPFHSE-set environment. The merits and superiority of the new algorithms are presented by comparing them with some existing fuzzy decision-making models. According to the proposed FPFHSE-set-based decision-making approaches, the experts have more freedom in specifying their preferences and thoughts according to their expertise, and they can process new types of data. Therefore, this paper presents a state-of-the-art improvement that provides a holistic view to understand and handle the multi-attribute decision-making issues focused on the objective of classifying alternatives according to multiple attributes by multiple experts.