Joint distribution route optimization of vehicle and drone based on NSGA Ⅱ

In order to reduce the distribution cost of terminal logistics and improve customer satisfaction, a multi-objective vehicle and drone joint distribution route optimization model is proposed to minimize the total cost of distribution and maximize the average satisfaction of customer under the constraints of time window, vehicle load, drone load and the longest flight distance. The pareto solution set is obtained by the elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ), then the entropy weight method is used to determine the weights of each objective function, and finally the optimal distribution scheme is obtained. The experimental results show that the joint distribution model of vehicle and drone can effectively reduce distribution cost and improve the customer satisfaction compared with the traditional vehicle distribution mode.