Aimming at the logistics distribution process,the traditional path optimization algorithm is not sensitive to traffic congestion,weather conditions,and environmental factors,which leads to inefficiency and unexpected conditions of vehicles in logistics distribution. This paper proposes a path optimization algorithm for logistics distribution based on deep learning and reinforcement learning,which is based on self-coding neural network and sample data,trains the model and predicts its substitution value,and fuses it with the road network to form a weighted road network. This paper adopts the method of combining deep learning and reinforcement learning,through continuous learning and optimization,so that the system can dynamically adjust the distribution task according to the actual situation,and gradually improve the distribution efficiency in a short period of time.
关键词
深度学习/强化学习/物流配送/路径调度优化
Key words
deep learning/reinforcement learning/logistics distribution/route scheduling optimization
维普
万方数据