Induced generalized Choquet aggregating operators with linguistic information and their application to multiple attribute decision making based on the intelligent computing

Abstract

In this paper, we investigate the multiple attribute decision making problems in which the attribute weights are usually correlative, attribute values take the form of linguistic variables, some new methods are developed. Then a new aggregation operator called induced generalized 2-tuple linguistic choquet ordered averaging (IG-2TCOA) operator is proposed, and some desirable properties of the IG-2TCOA operator are studied, such as idempotent, commutative, monotonic and bounded. An IG-2TCOA operators-based approach is developed to solve the MADM problems. Finally, an illustrative example for evaluating the performance of the mechanical products is given to verify the developed approach and to demonstrate its practicality and effectiveness.