Nachammai. AL, Dhavamani. M, Usha. B; International Journal of Advance Research, Ideas and Innovations in Technology © 2018, www.IJARIIT.com All Rights Reserved Page | 290 ISSN: 2454-132X Impact factor: 4.295 (Volume 4, Issue 2) Available online at: www.ijariit.com A Comparative Study of the Methods of Solving Intuitionistic Fuzzy Linear Programming Problem Dr. AL. Nachammai nachusenthilnathan@gmail.com Kongu Engineering College Perudurai, Tamil Nadu Dr. M. Dhavamani md@kongu.ac.in Kong Engineering College Perudurai, Tamil Nadu Dr. B. Usha usha_b@kongu.ac.in Kongu Engineering College Perudurai, Tamil Nadu ABSTRACT Ranking Intuitionistic Fuzzy Numbers plays an important role in decision making and information systems. Many researchers have proposed different ranking functions, score functions and signed functions for ranking Intuitionistic Fuzzy Numbers but unfortunately every method produces some anti-intuitive results in certain places. In this paper we compare different methods of ranking of Intuitionistic Fuzzy Number to solve Intuitionistic Fuzzy Linear Programming. Keywords: Fuzzy Linear Programming, Intuitionistic Fuzzy Linear Programming, Triangular Intuitionistic Fuzzy Number, Ranking Methods. 1. INTRODUCTION Linear programming is a technique used for determine an optimum schedule of interdependent activities based on the available resources. In many practical situations, the decision maker may not be in a position to specify the objective function and the constraints functions precisely in crisp environment but rather can specify them in intuitionistic fuzzy sense. Intuitionistic fuzzy linear programming, a new concept and method of uncertainty linear programming is put forward on the basis of IFS, which is a development of fuzzy linear programming. Since this method can consider both the degree of acceptance and rejection of objectives and constraints, it acts as the richest apparatus for solving linear programming problem, In addition, it makes fuzzy process for objective and constraint more meticulous and avoids high rank uncertain factors. Fuzzy set theory has been pioneered by Zadeh (1965). Fuzzy linear programming, an offspring of fuzzy set, is first formulated by Zimmermann (1978). Atanassov (1983, 1986) has generalized the notion of Zadeh’s fuzzy set to the concept of IFS which includes the degree of membership, non-membership and hesitation degree of an element in a set. Mahapatra & Roy (2009) proposed the arithmetic operations for intuitionistic fuzzy numbers. Many authors such as S.K.Bharati and A.Nagoorgani have studied Intuitionistic fuzzy linear programming. The author AL.Nachammai have proposed many methods to solve Intuitionistic fuzzy linear programming. The work present in this paper is based on a comparative study of methods of solving Intuitionistic Fuzzy Linear Programming. 2. INTUITIONISTIC FUZZY LINEAR PROGRAMMING Definition 2.1 Intuitionistic fuzzy linear programming is defined as Max I j I j n j x c ~ ~ 1   Subject to the constraints 0 ~ ,..., 2 , 1 ~ ~ ~      I j I i I j I ij n i j x m i b x a where                      j j j j j j j j I j c c c c c c c c c 4 3 2 1 4 3 2 1 , , , ; , , , ~