| dc.description.abstract | The optimization of the multi-depot Vehicle Routing Problem (VRP) with time windows
is a complex issue within the logistics and transportation field that traditional optimization
methods struggle to solve due to its complexity. In contrast, Genetic Algorithm (GA) is a
metaheuristic optimization method that has shown promising results in addressing various
optimization issues, including the VRP. This research aims to implement GA for solving
the multi-depot VRP with time windows and assess its performance regarding solution
quality and computation time. The problem is transformed into a set of chromosomes, and
genetic operators are employed to generate novel solutions. The fitness of each solution is
assessed using multiple objective functions, including the total distance traveled, the
number of vehicles utilized, and the total delivery time. The findings indicate that GA can
identify nearly optimal solutions in a reasonable amount of time and handle dynamic and
uncertain environments. Future studies may explore the potential of integrating machine
learning algorithms and other metaheuristic optimization techniques to enhance the
performance of GA in solving the multi-depot VRP with time windows. | en_US |
