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

TitleOptimization Algorithm Research of Logistics Distribution Path Based on the Deep Belief Network
Publication TypeConference Paper
Year of Publication2018
AuthorsLi, W., Li, S., Zhang, X., Pan, Q.
Conference Name2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)
Keywordsactual logistics distribution, ant algorithm, belief networks, Collaboration, composability, Data models, deep belief network, Human Behavior, Logistics, Logistics distribution, Metrics, optimisation, Optimization, optimization algorithm, Path optimization, policy-based governance, Prediction algorithms, pubcrawl, resilience, Resiliency, road traffic, Roads, Scalability, time-share traffic network, Traffic forecast, traffic forecast model, Training, urban traffic, Vehicles

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.

Citation Keyli_optimization_2018