Research on Vehicle Routing Problem with Time Window based on Improved Genetic Algorithm

Abstract

This article conducts a detailed study on the vehicle routing problem with time window constraints. We constructed an objective function for the vehicle routing problem with time windows, established a mathematical model, and proposed an improved genetic algorithm to solve the problem. The algorithm first constructs a chromosome encoding method, designs a heuristic initialization algorithm to generate a better initial population, and determines the fitness function. During the operation of the algorithm, selection, crossover, and mutation operations are designed to generate offspring populations, enhancing the diversity of the population and avoiding premature convergence of the algorithm. Meanwhile, in order to improve the optimization and local search capabilities of genetic algorithms, this paper constructs a local search operation. Finally, the algorithm implements an elite retention strategy on the parent population and reconstructs a new population. We conducted simulation experiments on the algorithm using MATLAB and selected examples from the Solomon dataset for testing. The simulation experiment results have verified that the improved genetic algorithm is feasible and effective in solving vehicle routing problems with time windows.