Multi-objective linguistic optimization: Extensions and new directions using 2-tuple fuzzy linguistic representation model

Multi objective linguistic optimization is a useful mathematical technique to solve problems that interdependent criteria. In such problems, values of the objective functions may be unknown at some points, when the link between the variables and the objective functions are defined linguistically through if-then rules. While solving this type of problems, Tsukamoto based reasoning method has proved useful for converting objective function to a crisp form, and then using the resulting objective function to solve by any traditional optimization technique. However, this method suffers from a drawback that the resulting solution is in numeric form whereas it should have been in linguistic form, owing to the linguistic definition of if-then rules. So, here we propose 2-tuple fuzzy linguistic representation model based method for solving the Multi objective linguistic optimization problem. We demonstrate the novelty of our approach through a suitable example. We also prove that the proposed approach generates unique recommendation in linguistic form.