Abstract: For the dynamic vehicle routing problem in logistics distribution,this paper built a multi-objective and dynamic mathematical programming model synthesizing dynamic demands,the effects on the road network,vehicle sharing,time window and customer satisfaction.This model can describe modern logistics distribution better.Meanwhile,the paper put forward a two-phase solving strategy for it.In the first phase,multi-objective hybrid particle swarm optimization is adopted to get preliminary Pareto solutions.The algorithm uses the modified updating strategy of particle states and simulated annealing operation to improve the searching performance of particles,and uses adaptive grid technique to maintain the dispersion of solutions.In the next phase,greedy insertion and variable neighborhood search are applied to adjust routes according to the changes in demand.The experimental results show that the two-phase algorithm has better exploring ability in solution space, and it can also converge to the global optimum rapidly,and satisfy the real-time requirement.

Key words: Logistics distribution,Vehicle routing problem,Hybrid particle swarm optimization,Simulated annealing,Pareto solution