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In real world, we come across transportation problems, wherein the associated data involves some sort of uncertainty, which at times can be most appropriately represented in the form of interval numbers. Since ordering of intervals involves complexity, hence, we have used fuzzy concept to solve Interval-Valued Transportation Problems (IVTPs). We have proposed new fuzzification methods for conversion of interval number to trapezoidal, pentagonal, hexagonal and heptagonal fuzzy numbers, thereby converting IVTP to Fuzzy Transportation Problem (FTP). Further, we have proposed new ranking functions for conversion of these fuzzy numbers to crisp numbers, which can also be used in other fields of decision making. The crisp-valued transportation problem is then solved using Vogel’s Approximation Method followed by Modified Distribution method. Numerical illustration for the proposed algorithm is given in a later section. The solutions obtained for these examples are used to approximate the solutions for octagonal, nonagonal and decagonal FTPs corresponding to IVTP, using Newton’s Polynomial. On the basis of these solutions, ordering of effectiveness of various types of fuzzy numbers in solving IVTP is done. Lastly, comparison is made between the optimal solutions obtained by various methods. The proposed method can be applied to industrial transportation problems in which the difference between the actual and the proposed demand and supply is quite significant.

In this paper, we introduce a new method to solve Interval-Valued Transportation Problem (IVTP) to deal with those problems of transportation wherein the information available is imprecise. First, a newly proposed fuzzification method is used to convert the IVTP to octagonal fuzzy transportation problem and then with the help of ranking function proposed in this paper, the fuzzy transportation problem is converted into crisp transportation problem. Lastly, Initial Basic Feasible Solution (IBFS) of this problem is obtained using Vogel’s Approximation Method and the solution is improved using Modified Distribution (MODI) method. A numerical example with interval data is solved using the proposed algorithm to make comparison of the solution with some other methods. Also, a numerical example with parameters in the form of octagonal fuzzy numbers is illustrated to compare the effectiveness of the proposed ranking technique. The proposed fuzzification and ranking technique can be used in the other fields of decision making dealing with the data in the same form as considered in this paper.