Open AccessA decision making algorithm for wind power plant based on q-rung orthopair hesitant fuzzy rough aggregation information and TOPSIS
Author(s) -
Attaullah Attaullah,
AUTHOR_ID,
Shahzaib Ashraf,
Noor Rehman,
Asghar Khan,
Choonkil Park,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022292
Subject(s) - multiple criteria decision analysis , topsis , computer science , rough set , fuzzy logic , mathematical optimization , wind power , fuzzy set , renewable energy , ideal solution , data mining , operations research , mathematics , artificial intelligence , engineering , electrical engineering , physics , thermodynamics
Wind energy is one of the most significant renewable energy sources due to its widespread availability, low environmental impact, and great cost-effectiveness. The effective design of ideal wind energy extraction areas to generate electricity is one of the most critical issues in the exploitation of wind energy. The appropriate site selection for wind power plants is based on the concepts and criteria of sustainable environmental advancement, resulting in a low-cost and renewable energy source, as well as cost-effectiveness and job creation. The aim of this article is to introduce the idea of q-rung orthopair hesitant fuzzy rough set (q-ROHFRS) as a robust fusion of q-rung orthopair fuzzy set, hesitant fuzzy set, and rough set. A q-ROHFRS is a new approach towards modeling uncertainties in the multi-criteria decision making (MCDM). Various key properties of q-ROHFRS and some elementary operations on q-ROHFRSs are established. A list of novel q-rung orthopair hesitant fuzzy rough weighted geometric aggregation operators are developed on the basis of defined operational laws for q-ROHFRSs. Further, a decision making algorithm is developed to handle the uncertain and incomplete information in real word decision making problems. Then, a multi-attribute decision making method is established using q-rung orthopair hesitant fuzzy rough aggregation operators. Afterwards, a practical case study on evaluating the location of wind power plants is presented to validate the potential of the proposed technique. Further, comparative analysis based on the novel extended TOPSIS method is presented to demonstrate the capability of the proposed technique.