Appl. Math. Inf. Sci. 14, No. 3, 459-465 (2020) 459 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.18576/amis/140313 A New Technique for Solving Unbalanced Intuitionistic Fuzzy Transportation Problems A. Edward Samuel 1 , P. Raja 2 and Srinivasarao Thota 3,* 1 Department of Mathematics, Govt Arts College (A), Kumbakonam. Tamil Nadu, India 2 Department of Mathematics, Periyar University College of Arts and Science, Pappireddipatti, Dharmapuri, Tamil Nadu, India 3 Department of Applied Mathematics, Adama Science and Technology University, Post Box No. 1888, Adama, Ethiopia Received: 26 Oct. 2019, Revised: 18 Feb. 2020, Accepted: 14 Mar. 2020 Published online: 1 May 2020 Abstract: In this paper, a new method is proposed for solving unbalanced Intuitionistic fuzzy transportation problems assuming that a decision maker is uncertain about the precise values of transportation costs, demand and supply of the product. In this proposed method, transportation costs, demand and supply of the product are represented by triangular Intuitionistic fuzzy numbers. To illustrate it, a numerical example is solved and the obtained result is compared with the results of other existing methods. It is very easy to understand this method which can be applied to real life transportation problems. Keywords: Triangular Intuitionistic fuzzy numbers, Unbalanced Intuitionistic Fuzzy Transportation Problem (UIFTP), Optimal Solution. 1 Introduction Transportation problem is an important network structured in linear programming (LP) problem that arises in several contexts and has deservedly received a great deal of attention in the literature. Its central concept is to find the minimum transportation cost of a commodity to satisfy demands at destinations using available supplies at origins. Transportation problem can be used for a wide variety of situations such as scheduling, production, investment, plant location, inventory control, employment scheduling. In general, transportation problems are solved with the assumptions that the transportation costs and values of supplies and demands are specified in a precise way i.e. in crisp environment. However, in many cases, decision makers have no crisp information about the coefficients relevant to transportation problem. In these cases, the corresponding coefficients or elements defining the problem can be formulated by means of fuzzy sets, and the fuzzy transportation problem (FTP) appears in natural way. The basic transportation problem was originally developed by Hitchcock [1]. The transportation problems can be modeled as a standard linear programming problem, which can then be solved by the simplex method. However, because of its very special mathematical structure, it was recognized early that the simplex method applied to the transportation problem can be made quite efficient in terms of how to evaluate the necessary simplex method information (Variable to enter the basis, variable to leave the basis and optimality conditions). Charnes and Cooper [2] developed a stepping stone method which provides an alternative way of defining the simplex method information. Dantzig and Thapa [3] used simplex method as the primal simplex transportation method. An Initial Basic Feasible Solution (IBFS) for the transportation problem can be obtained using the North- West Corner rule NWCR [2], Matrix Minima Method MMM [2], Vogel’s Approximation Method. We can find initial basic feasible solution using Vogel’s Approximation Method VAM [2]. Several authors attempted Vogel’s Approximation method for obtaining initial solutions to the unbalanced transportation problem. Shimshak [4] proposed a modification (SVAM) which ignores any penalty that involves a dummy row/column. Goyal [5] suggested another modification in (GVAM) where the cost of transporting goods to or from a dummy point is set equal to the highest transportation cost in the problem, rather than to zero. The method proposed by Ramakrishnan [6] consists of four steps of reduction and * Corresponding author e-mail: srinithota@ymail.com & srinivasarao.thota@astu.edu.et c  2020 NSP Natural Sciences Publishing Cor.