An adaptive evolutionary algorithm for bi-level multi-objective VRPs with real-time traffic conditions
The research of vehicle routing problem (VRP) is significant for people traveling and logistics distribution. Recently, in order to alleviate global warming, the VRP based on electric vehicles has attracted much attention from researchers. In this paper, a bi-level routing problem model based on electric vehicles is presented, which can simulate the actual logistics distribution process. The classic backpropagation neural network is used to predict the road conditions for applying the method in real life. We also propose a local search algorithm based on a dynamic constrained multi-objective optimization framework. In this algorithm, 26 local search operators are designed and selected adaptively to optimize initial solutions. We also make a comparison between our algorithm and 3 modified algorithms. Experimental results indicate that our algorithm can attain an excellent solution that can satisfy the constraints of the VRP with real-time traffic conditions and be more competitive than the other 3 modified algorithms.
The file attached to this record is the author's final peer reviewed version.
Citation : Chen, B., Li, C., Zeng, S., Yang, S. and Mavrovouniotis, M. (2021) An adaptive evolutionary algorithm for bi-level multi-objective VRPs with real-time traffic conditions. 2021 IEEE Symposium Series on Computational Intelligence, December 2021.
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes