A decision analytics approach for sustainable urbanization using q-rung orthopair fuzzy soft set-based Aczel–Alsina aggregation operators

Abstract

Benefits of urbanization include increased economic opportunities, better access to technology, healthcare, and education, as well as a better standard of living. The generalized extension of the q-rung orthopair fuzzy set in combination with the soft set (SS) is introduced to determine which location is most likely to be favourable for urban growth. The Aczel–Alsina aggregation operators (AA’AOs) for q-rung orthopair fuzzy soft set (q-ROFSS) are formulated. The generalized nature of q-ROFSS is due to flexibility in the index of membership and non-membership, which provide decision-makers more freedom to express their opinions. The developed AA’AOs are based on the Aczel–Alsina (AA) t-norm and t-conorm that emphasize parameter variability. Important properties of these operators are studied. A novel approach based on q-ROFSS is established. The approach is tested with a case study problem related to urbanization. In this scenario, a company is searching for the best possible area to develop a housing society. The results show that the approach is highly valuable and easily applicable. The stability of the operators is examined through comparative analysis. The findings of sensitivity analysis show that increasing parameters in q-ROFSS leads to diminishing the impact of the non-membership operation, indicating geometric expansion and mathematical rebalancing of dominance between operations.