Long-distance Distribution Path Planning Method Using Deep Reinforcement Learning for Smart Logistics Parks
Reasonable planning of logistics long-distance distribution path can save the number of transportation vehicles and ease the traffic pressure.However,the current long-distance distribution path planning method of smart logistics park has the problems that the planning path is not optimal and the planning time is long.Therefore,we propose a long-distance distribution path planning method based on deep reinforcement learning for smart logistics parks.Firstly,the shortest distribution distance is taken as the planning goal to build a long-distance distribution path planning model of the smart logistics park;then,the customer state information is fused and processed to complete the construction of the attention mechanism;finally,the route planning capability of the planning model is iteratively trained using deep reinforcement learning,and the output of the trained model is taken as the best distribution path planning result.The experimental results prove that the best distribution path can be effectively derived using this method,and the path length is basically controlled within the range of 4m～8m,and the time corresponding to obtaining the best average path length is less than 20ms,with better planning performance and shorter planning time,which has better planning effect.
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