Fuzzified Multi-Objective Transportation Problem: A Real coded Genetic Algorithm approach to the Compromised near-to-Optimal solution

In this article a comparative study on the effectiveness of Real-coded Genetic Algorithm (RGA) approach in finding the compromised near-to-optimal solution and to by-pass the allocation rule of (m+n−1) for a Fuzzified Multi-Objective Transportation Problem (FMOTP) is discussed. For this purpose, some benchmark problems with well-defined membership function were considered and several changes have been made in the execution of Genetic Algorithm with real codes. The performance of the RGA Approach in obtaining the compromised near-to-optimal solution is compared with the traditional methods, namely as Interactive approach and Fuzzy programming approach. The obtained results reveal that, the applied RGA technique is more advantageous and effective after incorporating some changes according to the problem statement. This approach gives more than one compromised near-to-optimal solutions as well as bypasses the allocation rule of (m +n − 1), which is a great advantage in solving FMOTP. Two numerical examples have been considered and discussed to demonstrate this advent.