An Advanced Fuzzy Optimization Model for Sustainable Supply Chain Decisions Using Picture Fuzzy Soft Aczel-Alsina Aggregation Operators

Abstract

This paper introduces a novel fuzzy optimization model designed to solve complex Multi-Criteria Decision-Making (MCDM) problems in sustainable supply chain management. Building on previous research in advanced fuzzy set applications, the core innovation lies in developing new aggregation operators within the Picture Fuzzy Soft Set (PFSS) environment powered by Aczel-Alsina triangular norms. We propose the Picture Fuzzy Soft Aczel-Alsina Weighted Averaging (PFSAAWA) and Geometric (PFSAAWG) operators, which serve as the mathematical optimization engine for synthesizing uncertain and hesitant expert judgments. The model extends recent work in fuzzy decision systems by providing a formal optimization framework with proven mathematical properties. The model showcases consistent supplier identification (S ranked first with score 0.235) throughout all cases and exhibits significant parameter variations. The study is an excellent example of a technologically advanced and highly reliable tool for green procurement decision-making under uncertainty for the supply chain managers.