Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Applications in Integrated Diagnosis

The type-1 ordered weighted averaging (T1OWA) operator has demonstrated the capacity for directly aggregating multiple sources of linguistic information modeled by fuzzy sets rather than crisp values. Yager's (OWA) operators possess properties idempotence, monotonicity, compensativeness, and commutativity. This article aims to address whether or not T1OWA these when inputs associated weights are instead numbers. To this end, a partially relation is defined based on maximum (join) minimum (meet) sets, an alpha-equivalently-ordered groups proposed. Moreover, as extension orness andness OWA operator, joinness meetness formalized, respectively. Then, concepts representation theorem operators, we prove that hold same possess, i.e., Various numerical examples case study diabetes diagnosis provided validate theoretical analyses in uncertain improving integrated diagnosis,