Optimizing decision-making in electric power system selection: A generalized approach based on Hamacher aggregation operators for q-rung orthopair fuzzy soft sets

Aurang Zeb, Waseem Ahmad, Muhammad Asif, Vladimir Simic, Tapan Senapati and Muzhou Hou

Applied Energy, 2024, vol. 367, issue C, No S0306261924007888

Abstract: Identifying the most suitable power source that is cost-effective, environmentally friendly, socially accepted, and capable of ensuring long-term sustainability represents a critical decision-making (DM) problem. Existing models based on traditional fuzzy sets and specific norms cannot describe complex issues such as selecting the best electric power system (EPS). These decision models can also be challenging, especially when transparency and regulatory compliance are required. This study introduces a novel operational framework based on Hamacher aggregation operators for q-rung orthopair fuzzy set soft sets. It aims to help decision-makers identify appropriate policies to overcome the shortfall of electric power. The advantages of the introduced decision support tool are better discrimination ability, well suited for handling extreme values in data, greater flexibility in representing complex DM scenarios, and parametrization. We also demonstrate the practical application of our approach to a real-world problem related to selecting the most suitable EPS in Pakistan. The selection is made from the set of five EPSs and is based on critical aspects like social acceptance, environmental impact, and cost-effectiveness. The findings show that the developed approach effectively identifies the best EPS. Tuning variables like q and ℓ allows for testing the sensitivity of the developed operators. The results showed that higher values of q make the uncertainty more evenly represented, which is essential for specific uses. The comparison with the existing approaches suggests the superiority of the proposed analytic framework.

Keywords: Electric power system; Hamacher aggregation operators; q-rung orthopair fuzzy soft sets; Decision-making (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924007888
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:367:y:2024:i:c:s0306261924007888

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123405

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().