Evaluation of food waste treatment techniques using aczel alsina based MAGDM model in the q-rung orthopair fuzzy soft structure.

Food waste is a major obstacle in managing inequality, optimizing living conditions, and promoting prosperity, specifically among the world's most starving economies. Its influences stretch to preventing food supply; it alters financial maturation, complicates environmental issues decomposition, and incorporates raised food operating expenses. Monitoring food waste is implicitly challenging due to confusion arising from its authenticity, extent, geographic location, and schedule; all factors prevent decision-making procedures. This research proposes Aczel-Alsina operational laws to solve the obstacles and intrinsic uncertainty in a q-rung orthopair fuzzy soft sets (q-ROFSS) structure. Also, two novel Aczel-Alsina aggregation operators (AOs) such as q-rung orthopair fuzzy soft aczel-alsina weighted average (q-ROFSAAWA) and q-rung orthopair fuzzy soft aczel-alsina weighted geometric (q-ROFSAAWG) operators are developed with their desirable properties. These operators encourage more accurate and sustainable consolidation of unsure data in multi-attribute group decision-making (MAGDM) mechanisms. A real-life example highlights the proposed method's feasibility and efficacy in identifying the most optimal food waste treatment technologies (FWTT). The comparative study confirms this methodology's validity, exactitude, and feasibility, clarifying its better accuracy and feasibility as compared to other methods. The outcomes demonstrate that the most effective technique for facilitating food waste treatment in the FWM is incineration.