Abstract
Aiming at the problems of low efficiency and slow iteration convergence of existing distribution vehicle routing optimisation algorithms, a logistics distribution vehicle routing optimisation algorithm based on cloud model is proposed. Data are trained and learned using the deep belief network (DBN) model. According to label data and model training results set manually, the road condition of logistics distribution route is predicted. Combined with genetic algorithm and cloud model theory, the real-time solution and update of time-sharing weights of time-sharing weighted network paths are realised, and the global optimal solution is obtained. The experimental results show that compared with the existing algorithms, the logistics distribution time of the research algorithm varies in the range of 70 s-37 s, and the efficiency of logistics distribution is high. When the number of iterations is 1,000, the path length of this algorithm is 2,400 km, and the iteration convergence is fast.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Information Technology and Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
