Abstract

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.

Highlights

  • In recent years, the vigorous development of ecommerce industry has promoted the development of logistics industry

  • In the process of actual logistics distribution, the traffic conditions directly affect the driving time of vehicles on the road network, which means that the dynamic change of traffic conditions is one of the main challenges for vehicle routing problem (VRP)

  • Construction of Real Road Network The research object is the bi-level VRP based on the actual traffic conditions for electric vehicles

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Summary

INTRODUCTION

The vigorous development of ecommerce industry has promoted the development of logistics industry. Vehicle routing problem (VRP) aims to reduce the cost of logistics and improve the satisfactions of customers. The key to 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 and so on; the key to the later goal is that the vehicle serves the customer within the customer’s time window. In order to make the VRP model applicable to real world, we propose a new bi-level VRP that combines the real road network topology, the multi-objective with a soft time windows, and delivery and picking up goods (BLMOVRPRTC).

MODELING OF BLMOVRPRTC
Modeling Construction of BLMOVRPRTC
Multi-objective Optimization
Framework of DCMOEA
Encoding of Solutions
Designing Local Search Operators
Adaptive Mechanism
EXPERIMENT
Performance of Optimization Methods
Contribution Degree of Local Search Operators
Findings
CONCLUSION
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