Enhancing Logistic Efficiency in Product Distribution through Genetic Algorithms (GAs) for Route Optimization

Abstract

This research highlights the significant potential of Genetic Algorithms (GA) as a powerful tool for optimizing logistics distribution routes. The utilization of GA has led to substantial improvements in route efficiency, resulting in cost reductions and shorter delivery times. Notably, the inclusion of customer satisfaction as a key parameter in route optimization emphasizes the importance of meeting customer expectations and ensuring timely deliveries. Additionally, the study recognizes the positive environmental implications of reduced travel distances and durations, indicating a favorable impact on environmental sustainability by reducing carbon emissions. Ethical considerations remain paramount, as the research employs anonymized data sources and adheres rigorously to industry standards to safeguard data privacy. Comparative analyses consistently favor GA over conventional distribution methods, reaffirming its capacity to generate more efficient routes. Overall, this investigation underscores the versatility and efficacy of Genetic Algorithms in addressing complex logistics distribution challenges, offering practical solutions that benefit businesses, customers, and environmental conservation alike.