Optimization Algorithm Research of Logistics Distribution Path Based on the Deep Belief Network

• Published in: 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) pp. 60 - 63
• Main Authors: Li, Wenjing, Li, Songzhao, Zhang, Xiangbo, Pan, Qiuxia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Data models; Deep belief network; Logistics; Logistics distribution; Optimization; Path optimization; Prediction algorithms; Roads; Traffic forecast; Training; Vehicles
• ISSN: 2473-3636
• DOI: 10.1109/DCABES.2018.00025

Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.