| dc.description.abstract | As supply chain networks become increasingly complex, optimizing logistics is critical
for industries to maintain competitiveness and adapt to dynamic market demands.
Traditional route optimization methods often struggle to address real-time variables
such as traffic congestion, unpredictable weather, and evolving customer requirements,
resulting in inefficiencies. This study investigates 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 identify their limitations in
managing dynamic, multi-factor logistics environments. The findings highlight that
Genetic Algorithms excel in integrating real-time data, enabling more efficient and
adaptable delivery route optimization. Real-world applications across various industries
demonstrate notable reductions in delivery times, improved resource utilization, and
enhanced customer satisfaction. This study underscores the scalability and intelligence
of GA as a solution to modern logistics challenges, providing valuable insights for
advancing supply chain management practices. The implications suggest that GA offers
a transformative approach to addressing inefficiencies in complex logistics networks
and improving overall operational performance. | en_US |
