A Comprehensive Review: Enhance Logistics Performance by Optimizing Supply Chain Routes with Dynamic Factors using Genetic Algorithm

Abstract

As supply chain networks grow increasingly complex, achieving optimal logistics has become essential for industries to remain competitive and adapt to dynamic demands. Traditional route optimization methods often fail to accommodate real-time factors such as traffic congestion, unpredictable weather conditions, and shifting customer requirements, leading to inefficiencies in logistics performance. This study aims to address these challenges by exploring the potential of Genetic Algorithm (GA) as a robust solution for multi-objective route optimization. A thematic literature review was conducted to evaluate existing algorithms and models, revealing significant gaps in their ability to manage dynamic, multi-factor logistics environments effectively. The review identified that Genetic Algorithm excel in integrating real-time data, enabling the optimization of delivery routes with greater efficiency and adaptability. Real-world applications of GA in diverse industries demonstrated reductions in delivery times, improved resource utilization, and enhanced customer satisfaction. These findings establish GA as an intelligent and scalable approach to modern logistics challenges, offering significant implications for advancing supply chain management practices.