Abstract: Under the background of rapid development of e-commerce, the problem of logistics terminal distribution optimization has become the key to its operational efficiency. For the multi-depot e-commerce logistics distribution problem, firstly, considering the impact of actual loading on vehicle fuel consumption in the process of logistics distribution, a fuel consumption model is established, and combining the commitment delivery mechanism of e-commerce platform, delayed delivery time function is built. Then, the multi-objective multi-depot vehicle routing problem with minimizing logistics distribution cost and minimizing delayed delivery time is proposed, and a mixed integer programming model of the problem is established. Thirdly, considering the NP-Hard characteristics of the problem and the difficulty of solving multi-objective, the problem is solved by the multi-objective genetic algorithm based on decomposition. The algorithm uses matrix coding to design a heuristic initialization method based on greedy search strategy. At the same time, considering that the greedy search strategy is easy to fall into the local optimum, in the iterative process of the algorithm, some unfeasible solutions are allowed to exist to expand the search range of the solution space, and the crossover and mutation operators of the genetic algorithm are further designed. Finally, the numerical experiments are carried out with specific logistics distribution cases. The experimental results verify the effectiveness of the designed algorithm to solve the model, and also show the inverse relationship between logistics distribution cost and delayed delivery time. There is a trade-off between consumer satisfaction and logistics distribution costs. In order to achieve higher consumer satisfaction, it often means increasing the cost of logistics distribution. At the same time, the solution algorithm of this paper provides a solution to the problem of e-commerce terminal delivery.

Key words: multi-depots, fuel consumption, delayed delivery time, multi-objective, genetic algorithm based on decomposition