Modeling and Evolutionary Optimization for Multi-objective Vehicle Routing Problem with Real-time Traffic Conditions

Abstract

The study of the vehicle routing problem (VRP) is of outstanding significance for reducing logistics costs. Currently, there is little VRP considering real-time traffic conditions. In this paper, we propose a more realistic and challenging multi-objective VRP containing real-time traffic conditions. Besides, we also offer an adaptive local search algorithm combined with a dynamic constrained multi-objective evolutionary framework. In the algorithm, we design eight local search operators and select them adaptively to optimize the initial solutions. Experimental results show that our algorithm can obtain an excellent solution that satisfies the constraints of the vehicle routing problem with real-time traffic conditions.