Solving Logistics Distribution Vehicle Routing Problem Using Genetic Algorithm in MATLAB

In this article, we explore the methodology of solving the Logistics Distribution Vehicle Routing Problem using a MATLAB-implemented genetic algorithm. Within the logistics industry, optimizing delivery vehicle routes represents a critical operational challenge. Traditional approaches often require substantial time and human resources for route calculation and optimization, whereas genetic algorithms provide a more efficient computational solution. By simulating biological evolutionary processes through key operations including population initialization, fitness evaluation (typically using total distance or cost minimization), crossover (route recombination using techniques like ordered crossover), and mutation (route modifications through swap or inversion operations), the algorithm systematically optimizes vehicle paths to achieve faster and more efficient logistics distribution. This paper details the MATLAB implementation process, explaining core functions such as chromosome encoding for route representation, fitness function design for objective evaluation, and genetic operator customization. We will demonstrate how to program a genetic algorithm-based solution in MATLAB, complete with code structure explanation and performance analysis of the optimization results.