The container drayage problem for electric trucks with charging resource constraints

Abstract

Amidst the ongoing green transformation in transportation, the electrification of trucks has emerged as a pivotal strategy to address climate-related issues. This paper introduces the container drayage problem for electric trucks, considering the charging resource constraints. Electric trucks are assigned to serve a series of origin–destination tasks between terminals and customers. Each truck can opt between battery swapping and two charging modes: normal and fast, each featuring a nonlinear charging process. The paper addresses the charging queueing problem arising from limitations in charging resources, presenting a novel mixed integer programming model tailored to container drayage challenges for electric trucks. To tackle this challenging problem, we propose an enhanced adaptive large neighborhood search algorithm that integrates an exact method. In the first stage, routes are generated based on customized procedures without considering queueing charging to minimize overall operation costs. The second stage is triggered by the call frequency and condition coefficient, utilizing CPLEX to optimize further queueing charging strategies. The algorithm is applied to instances based on real-world task data obtained from logistics companies. A series of comparative experiments are conducted to validate the efficacy and ascertain the parameter configuration of the algorithm. Furthermore, we examine the influence of charge levels and numbers of replaceable batteries on overall expenses and conduct a comprehensive analysis of the application influence of electric trucks compared to conventional fuel trucks in terms of cost and emissions.