Abstract:
Abstract： Aiming at the problems of high distribution cost and low effective utilization rate of vehicles in the cold chain logistics distribution system， a multi-vehicle cold chain logistics route optimization model aiming at minimizing transportation cost and maximizing user satisfaction was constructed. At the same time， the impact of distribution time window and freshness of fresh goods on user satisfaction was considered， so as to no longer add extra costs to fresh goods that do not meet the time window distribution. Based on Elitist Non-dominated Sorting Genetic Algorithm（NSGA2）with elite strategies， a cluster initializing population method was designed， and an orderly crossover method was designed according to the characteristics of path coding. A repair strategy is designed to modify the infeasible solutions caused by constraints and guide them to search on the edge of constraints. Combined with the idea of Large Neighborhood Search （LNS） algorithm， it guides individuals to search in the neighborhood， increases the local search ability， and enriches the population diversity. The simulation results show that the Pareto frontier obtained by the algorithm is obviously superior to the traditional NSGA2 algorithm in multi-objective multi-vehicle routing optimization problem.

Key words: Key words： cold chain logistics route optimization, time window constraint, multi-objective, NSGA2, neighborhood search