Enhanced decision model for sustainable energy solutions under bipolar hesitant fuzzy soft aggregation information

Zaheer Ahmad; Shahzaib Ashraf; Shawana Khan; Mehdi Tlija; Chiranjibe Jana; Dragan Pamucar; Zaheer Ahmad; Shahzaib Ashraf; Shawana Khan; Mehdi Tlija; Chiranjibe Jana; Dragan Pamucar

doi:10.3934/math.2025198

AIMS Mathematics

2025, Volume 10, Issue 2: 4286-4321. doi: 10.3934/math.2025198

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Research article

Enhanced decision model for sustainable energy solutions under bipolar hesitant fuzzy soft aggregation information

1.
Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
2.
Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800 Riyadh 11421, Saudi Arabia
3.
Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 602105, Tamil Nadu, India
4.
Transport and Logistics Competence Centre, Vilnius Gediminas Technical University, Vilnius, Lithuania

Received: 01 August 2024 Revised: 14 December 2024 Accepted: 23 December 2024 Published: 28 February 2025
MSC : 03E72, 90B50

Energy sustainability is described as an ability to get the energy supplies without diminishing the ability of future generations to provide themselves with any energy. Preceded by the mentioned notion, this paper will be focused on bipolar hesitant fuzzy soft sets (BHFSS) related to the problem of energy sustainability. It actually was a proposed new mathematical method able to conquer ambiguity and uncertainty while determining the different choices in energy-related decisions. In this way, it lead to more informative and better choices to be made, thus leading to the utilization of sustainable energy systems. The paper introduced basic operations and comparison rules for BHFSS. Furthermore, algebraic norms-based aggregation operators were proposed to make the model more robust and flexible so that it was adaptable to a wide range of energy sustainability decisions. Main characteristics of the BHFSS aggregation operators were discussed in detail. Last but not least, this paper also provided a comparison of the BHFSS-based approach with one of the most popular multi-criteria decision-making (MCDM) approaches known as compromise solution (CoCoSo). This comparison confirmed how BHFSS can control for uncertainty and how it can reflect preferences in a mapped way, which afforded it strengths in uses like choosing renewable power and strategy for lowering $ CO_{2} $ emissions.
- bipolar hesitant fuzzy soft set,
- algebraic aggregationoperators,
- group decision making
Citation: Zaheer Ahmad, Shahzaib Ashraf, Shawana Khan, Mehdi Tlija, Chiranjibe Jana, Dragan Pamucar. Enhanced decision model for sustainable energy solutions under bipolar hesitant fuzzy soft aggregation information[J]. AIMS Mathematics, 2025, 10(2): 4286-4321. doi: 10.3934/math.2025198

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Abstract

Energy sustainability is described as an ability to get the energy supplies without diminishing the ability of future generations to provide themselves with any energy. Preceded by the mentioned notion, this paper will be focused on bipolar hesitant fuzzy soft sets (BHFSS) related to the problem of energy sustainability. It actually was a proposed new mathematical method able to conquer ambiguity and uncertainty while determining the different choices in energy-related decisions. In this way, it lead to more informative and better choices to be made, thus leading to the utilization of sustainable energy systems. The paper introduced basic operations and comparison rules for BHFSS. Furthermore, algebraic norms-based aggregation operators were proposed to make the model more robust and flexible so that it was adaptable to a wide range of energy sustainability decisions. Main characteristics of the BHFSS aggregation operators were discussed in detail. Last but not least, this paper also provided a comparison of the BHFSS-based approach with one of the most popular multi-criteria decision-making (MCDM) approaches known as compromise solution (CoCoSo). This comparison confirmed how BHFSS can control for uncertainty and how it can reflect preferences in a mapped way, which afforded it strengths in uses like choosing renewable power and strategy for lowering $ CO_{2} $ emissions.

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