Research on kruskal crossover genetic algorithm for multi-objective logistics distribution path optimization

Abstract

To effectively optimize multi-objective logistics distribution path, the distance and distance related customer satisfaction factor are used as the objective function, a novel kruskal crossover genetic algorithm (KCGA) for multi-objective logistics distribution path optimization is proposed. To test the optimization results, the terminal distribution model and the virtual logistics system operating model are built. Experiment results show that, compared with basic genetic algorithm (GA), the run time of KCGA takes a slightly higher. But the average distribution distance and the best distribution distance are reduced by 6%-8%. Achieve the goal of multi-objective logistics distribution path optimization.