| dc.description.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. | en_US |
