Optimization of urban terminal delivery routes using fuzzy genetic algorithm and its practical application

Abstract

As urban populations grow and delivery demands surge, the computational complexity of route optimization problems escalates significantly. Traditional algorithms often struggle with low efficiency, making it challenging to achieve satisfactory solutions within a reasonable timeframe. To address these limitations, this paper introduces a fuzzy genetic algorithm (FGA) that integrates fuzzy logic to model uncertainties inherent in the delivery process, such as traffic congestion, weather disruptions, and demand fluctuations. By leveraging the multi-objective optimization capabilities of genetic algorithms, the proposed FGA comprehensively considers key factors such as delivery time, transportation costs, and customer satisfaction to generate optimal routes. The practical application of this approach demonstrates its effectiveness: the optimized delivery routes significantly reduce delivery times and transportation costs while enhancing customer satisfaction levels. Statistical analysis reveals p-values below 0.05, confirming the significant impact of the FGA on urban terminal delivery optimization. This research not only addresses the computational inefficiencies of traditional methods but also provides a robust framework for handling dynamic and uncertain urban environments. The integration of fuzzy logic and genetic algorithms represents a pioneering step toward sustainable urban logistics, offering both economic value—through cost savings—and social benefits—via improved service quality. In summary, the fuzzy genetic algorithm emerges as a powerful tool for modern urban delivery systems, enabling smarter decision-making and fostering greener, more efficient cities.