INTERNATIONAL JOURNAL OF ADVANCE RESEARCH
IN MULTIDISCIPLINARY

Transportation problems are fundamental in operations research and logistics, often involving conflicting objectives such as cost minimization, time reduction, and environmental impact. Traditional optimization techniques fall short in handling multiple objectives under uncertain and imprecise data. This study proposes a novel hybrid methodology that integrates Fuzzy Programming (FP) with Genetic Algorithms (GA) to solve Multi-Objective Transportation Problems (MOTPs) under uncertainty. The fuzzy model handles vagueness in the data while the genetic algorithm optimizes conflicting objectives. A set of real-world case studies demonstrates the model's effectiveness in producing robust, adaptive, and optimal solutions. This research significantly advances the decision-making process in transportation systems under uncertainty and sets the foundation for future intelligent logistics optimization frameworks.