Adaptive Elitist Genetic Algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

Yanfei Zhu,Yonghua Wang,Kwang Y Lee

doi:10.1109/access.2021.3053285

Yanfei Zhu, Yonghua Wang + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3053285

Copy DOI

Abstract

This paper applies the elitist genetic algorithm to the electric vehicle routing problem with time window. In initialization, the paper proposes an improved neighbor routing initialization method for adaptive elitist genetic algorithm. The improved neighbor routing method is used to select the nearest EV customer as the next route to be scheduled and make the route start from the suitable first customer in the initialization of the elitist GA. It makes the scheduled route begins with a neighboring directionality, which can be inherited in selection, crossover, and mutation operations. For effective convergence, new adaptive crossover probability and mutation probability are provided to make the algorithm converge faster. Experimental studies on randomly distributed customers and Solomon benchmark cases show the effective performance of the algorithm. The algorithm is demonstrated in the simulation of a U.S. Postal Service system.

Highlights

Nowadays, as consumption and purchasing activities are increasing, many transport vehicles are used for delivering goods or cargos from depot to customers
Electric vehicles (EV) are better for energy saving and reducing pollutants than combustion-engine vehicles, and many countries require electric vehicles (EV) to be used in their transportation systems
NR-elitist genetic algorithm (EGA) will select 1→4 as the routing schedule for EV in the initialization. This initialization method makes the scheduled route begins with a neighboring directionality. This feature can be inherited in selection, crossover, and mutation operations, make the neighbor routing method for EGA (NR-EGA) faster to find the global optimum

Summary

INTRODUCTION

As consumption and purchasing activities are increasing, many transport vehicles are used for delivering goods or cargos from depot to customers. The neighbor routing method for EGA (NR-EGA) is used to select the nearest neighbor customer as the service route to be scheduled. The novel contribution of this paper is summarized as follows: 1) An improved neighbor routing initialization method for adaptive EGA is proposed It enables the NR-EGA select the nearest neighbor customer as the service route with the suitable first customer in the initialization. As in a real routing schedule, a vehicle often chooses a neighboring customer as its service priority, the paper proposes the neighbor routing initialization method for EGA (NR-EGA). This initialization method makes the scheduled route begins with a neighboring directionality This feature can be inherited in selection, crossover, and mutation operations, make the NR-EGA faster to find the global optimum

THE FIRST CUSTOMER ALTERNATIVES IN INITIALIZATION

SIMULATIONS FOR RANDOMLY DISTRIBUTED CUSTOMER

COMPARISON WITH SOLOMON BENCHMARK DATA

CONCLUSION