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 multiobjective 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.

I. Introduction

In recent years, the vigorous development of ecommerce industry has promoted the development of logistics industry. In 2019,Chinese total social logistics cost accounts for 14.7% of gross domestic product while the proportion remains at 8% to 9% in developed countries. Vehicle routing problem (VRP) aims to reduce the cost of logistics and improve the satisfactions of customers. The aim of the former goal is to reduce the number of used vehicles, select the appropriate type of vehicles, reduce the energy consumption and the driving distance. The aim of the later goal is that the vehicle serves the customer within the customer's time window.