Model for selective vehicle problem considering mixed fleet with capacitated electric vehicles - Jiacheng Li; Masato Noto; Yang Zhang; Jia Guo (Scientific Reports)
Abstract As the introduction of electric vehicles has significantly changed the composition of fleets for the majority of logistics providers, addressing the last-mile delivery issue with a mixed fleet of conventional and electric vehicles has become an urgent need. Key problems for delivery service providers include how to effectively reduce energy consumption during delivery and improve the daily delivery completion rate. This paper considers the self-loading constraints and energy consumption constraints of different types of trucks and establishes a multi-objective optimization model aimed at maximizing service completion, minimizing service energy consumption, and minimizing emission. Subsequently, an adaptive large neighborhood search algorithm, improved on the basis of simulated annealing principles and local search algorithms, is proposed. The performance and computational efficiency of this algorithm are validated through comparative analysis with other classic optimization algorithms on test instances. It has been observed that ALNS performs commendably in addressing the problem presented in this text. Moreover, the algorithm proposed herein has optimized the solution by approximately 2% to 15% compared to traditional algorithms. Finally, using a medium-sized example with 250 demands and 10 vehicles, the impact of parameters such as the proportion of oil to electric vehicles and expired compensation prices on delivery completion and delivery costs is explored. The study reveals that the advantages of energy conservation and emission reduction can only be fully realized when the proportion of electric vehicles reaches 80%. The research results can provide decision-making references for logistics fleet managers at the current stage.