This paper investigates solving the transportation problem, with fuzzy demands and fuzzy supplies using a two-stage genetic algorithm (GA). At, the first, stage, we simulate a fuzzy number by distributing a fuzzy valve into certain partition points. We then use CA to evolve the values in each, partition point and the final valves represent the membership grade of that, fuzzy number. As a, result, vie obtain. the estimated values of all fuzzy demands and fuzzy supplies and the original fuzzy problem becomes a defuzzified instance. The best, solution to the defuzzified instance is then solved by the following stage via evolution process. The experimental results show that the proposed two-stage GA approach outperforms the other fuzzy approach to solving the transportation problem with fuzzy demands and fuzzy supplies.
