Abstract
It is vital to improve the efficiency of urban distribution with severe traffic congestion problems. However, few studies on urban distribution consider the impact of road conditions, which leads to less instructive results to the real-world problems. This paper considers the real travel time of the vehicles to minimize the total distribution time. Furthermore, hybrid brain storm optimization and large neighborhood search algorithm (BSO-LNS) are designed to solve this problem. The experimental results show that the proposed BSO-LNS algorithm is superior to peer competitors. The optimized routes are easier to obtain the global optima, which is suitable for solving the urban distribution problem under such complex road conditions.
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Acknowledgment
The work described in this paper was supported by Natural Science Foundation of Guangdong Province (Grant No. 2020A1515010749), Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau (Grant No. 2019KZDXM030), University Innovation Team Project of Guangdong Province (Grant No. 2021WCXTD002).
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Liu, J., Guo, N., Xue, B. (2023). Brainstorming-Based Large Scale Neighborhood Search for Vehicle Routing with Real Travel Time. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_28
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