Abstract
In this paper, the path planning algorithm in logistics based on improved NSGA-II is considered which aims to make shortest transportation distance, the least used vehicles and the lowest transportation cost under the constraints of the road condition, the goods demand, the vehicle capacity and the transportation miles. First, the constraint-insert method is introduced to accelerate population initialization. Secondly, the density information is brought into Pareto sorting to maintain distribution uniformity. In the end, the sub-path reversal operation is designed to ensure the population diversity and the good individual inheritance. The simulation results show that the improved NSGA-II has better convergence effect and convergence value than the standard algorithm.
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