Fuzzy Grey Wolf Optimizer Framework for Solid Transportation Problem

This research shows the optimization of solid transportation problem (STP) by combining the fuzzy logic with the Grey Wolf Optimizer (GWO). The suggested method is better than the standard Grey Wolf Optimizer (GWO) because it uses fuzzy logic to balance exploration and exploitation. This leads to more stable convergence and more accurate solutions. Both the traditional GWO and the Fuzzy-GWO are tested in a real-world transportation scenario with multiple sources, destinations, and modes of transportation. The numerical computations show that Fuzzy-GWO gives better and more reliable solutions than the standard GWO. Also, statistical tests of the cost and convergence data show that the improved robustness and better performance are true. These results confirm that Fuzzy-GWO is a valuable optimization tool for transportation planning in real-world STP scenarios characterized by uncertainty.