A 2‐Tuple Linguistic Dynamic OWAWA Aggregation Operator and its Application to Multi‐Attribute Decision Making

Abstract

A linguistic dynamic decision making problem reveals situations in which the decision data gathered in multiple periods is represented by means of linguistic values. To deal with linguistic variables in linguistic dynamic decision making problems, the 2-tuple linguistic model stands out among computational models because of its accuracy and interpretability. The selection of a suitable time-dependent 2-tuple linguistic aggregation operator is relevant due to its properties can highly modify the computing cost as well as results themselves and their accuracy and interpretability. This paper proposes a new 2-tuple linguistic dynamic hybrid weighted aggregation operator which is suitable to model different attitudes in decision making by simultaneously weighting the given arguments as well as their ordered positions. The novel 2-tuple Linguistic Dynamic Ordered Weighted Averaging–Weighted Average operator weights not only the importance of a particular time period, but also the importance of non-dynamic evaluations in such a time period. Eventually, a practical example is provided to illustrate the developed approach and to demonstrate its practicality and effectiveness.