Akademik Veri Yönetim Sistemi
IET INTELLIGENT TRANSPORT SYSTEMS, cilt.19, sa.1, 2025 (SCI-Expanded, Scopus)
Last-mile logistics increasingly adopt electric vehicles to address environmental concerns and reduce operational costs. Unlike classical vehicle routing problems, it is essential to consider charging stations in the route planning for electric vehicles. This study aims to investigate the effect of different charging strategies on last-mile delivery optimisation. The adaptive large neighbourhood search (ALNS) algorithm is proposed to solve large-scale problems. The results of the proposed algorithm are compared with the results of the mathematical model in small-scale problems, and the algorithm's performance is proven. The proposed algorithm contributes to electric vehicle route planning by providing effective results in solving large-scale problems. The test problems are solved with three different charging strategies: full charging, partial charging, and partial charging between 20-80% state of charge (SoC). Solutions have been obtained for the objective functions of the minimising total distance, the minimizing total time, and the minimising total energy consumption. The results of the experiments show that the average charging time is the lowest when the total travel time is minimised, the highest values are reached when the total distance is minimised, and more balanced results are provided when the energy consumption is minimised. These findings help logistics companies to determine the most appropriate charging strategy in terms of operational efficiency and cost optimisation.