Decision-Making-Based Solar Panel Selection: Sugeno-Weber Operators and Fermatean Fuzzy Distance Measure with AROMAN Methodology

Journal Article

2025

Published in: https://doi.org/10.1007/s12559-025-10456-8

Solar energy provides substantial benefits as compared to the fossil fuels as being renewable, reducing greenhouse gas
emissions, and providing decentralized power systems. The government of India is implementing several policy measures
to increase the production of solar energy with a strategic importance on solar power to fulfil the growing electricity requirements.
Choosing a suitable solar panel with longer assurances is an important and critical issue that ensures longevity and
reliability. It is a significant concern that requires to be thoroughly analyzed for producing solar power efficiently by means
of several key dimensions viz. mechanical, financial, electrical, and customer characteristics. This study aims to develop a
hybrid multi-criteria group decision-making framework for addressing the solar panel selection problem, including 5 solar
panel options over 12 criteria from 4 aspects. The proposed approach incorporates the Sugeno-Weber-weighted aggregation
operators, standard deviation (SD)-based model, rank sum (RS) model, and the alternative ranking order method accounting
for two-step normalization (AROMAN) under the context of Fermatean fuzzy sets. To illustrate the effectiveness of
introduced approach, it is employed to a case study of solar panel selection problem, followed by sensitivity and comparative
analyzes. The results show that the “Monocrystalline PERC solar panel” has the maximum preference among the others in
the process of solar panel selection problem. This study provides practical insights for policymakers by offering a systematic
technique for evaluating the solar panels based on different criteria and uncertain information.

• Publisher Name: SPRINGER
• Publishing City: USA
• Volume Number: 17
• Issue Number: 3
• Magazine \ Newspaper: Cognitive Computation
• Pages: 1 to 28