A Multi-Attribute Group Decision-Making Scheme Under q-Rung Orthopair Fuzzy Rough Aczel-Alsina Geometric Aggregation Operators with Applications in Sustainable Transportation

Increasing transportation-related emissions and congestion progressively challenge sustainable urban development. Within the context of the circular economy, shared mobility concepts, particularly those leveraging the vast logistics networks of postal operators, offer promising, environmentally friendly solutions. To support such initiatives and model uncertainty more effectively, we integrate the q-rung orthopair fuzzy rough set (q-ROFRS) model with Aczél-Alsina (AA) T-norms to create advanced aggregation operators (AOs). Specifically, we introduce three new AOs: q-rung orthopair fuzzy rough AA weighted geometric (q-ROFRAAWG), q-rung orthopair fuzzy rough AA ordered weighted geometric (q-ROFRAAOWG), and q-rung orthopair fuzzy rough AA hybrid weighted geometric (q-ROFRAAHWG). These operators expand the flexibility of traditional fuzzy models through their parametric mathematical structures. We scrutinize the fundamental mathematical features and the particular cases of the established operators. Moreover, an innovative multi-attribute group decision-making (MAGDM) framework is formulated within the q-ROFRS environment by integrating the proposed AOs. The robustness and practical applicability of the proposed framework are verified via a comprehensive case study on environmentally friendly transportation-sharing strategies. Additionally, its stability and effectiveness are validated by sensitivity analysis and comparative evaluations over existing schemes, underscoring its capability to deliver reliable and insightful outcomes for realistic decision-analytic dilemmas.