Fusion quality of intuitionistic fuzzy information aggregation operators with application to software development risk assessment

An effective intuitionistic fuzzy(IF) information fusion by aggregation operator(AO) must significantly reduce information uncertainty while minimizing information loss. However, the current academic research interest is focused on operational rules and related mathematical properties of OA, with little exploration of fusion quality. This study aims to develop novel AOs that simultaneously address uncertainty reduction and information loss minimization. First, we establish a set of fusion quality metrics based on uncertainty reduction and information loss minimization principles. Second, we introduce a series of innovative AOs derived from binary operations, with rigorous theoretical proofs demonstrating their enhanced fusion capabilities. Finally, through a case study on software development risk assessment, we empirically validate that our proposed operators outperform existing methods in terms of fusion quality for multi-criteria decision making (MCDM) within IF environments.