The use of eco-friendly energies has gained significant attention in recent years due to their high importance in preserving the environment. This study examines the vehicle routing problem, utilizing two categories of vehicles: owned vehicles and rented vehicles. The rented vehicles exclusively consist of electric vehicles, while the owned vehicles include both electric and fossil fuel-powered ones. To encourage the adoption of electric vehicles and discourage the use of fossil fuel-powered vehicles, government incentives are implemented. This approach aims to mitigate the harmful environmental impacts associated with fossil fuel consumption. This pioneering concept is formulated as the Close-Open Mixed-fleet Electric Vehicle Routing Problem with time window (COMF-EVRP), with a detailed mathematical framework provided. In the absence of real data, the performance of the proposed algorithm is evaluated using numerical examples involving 30 vehicles: 5 rental electric vehicles, 10 owned electric vehicles, and 15 fossil fuel-powered vehicles. The test problems differ in customer distribution, including random, clustered, and a combination of both scenarios. Key parameters such as vehicle capacities, customer demands, and charging stations were also considered. Due to the complexity of the mathematical model, a meta-heuristic approach based on the ant colony optimization algorithm is proposed to solve the problem. To improve the quality of the obtained solutions, they undergo a variable neighborhood search procedure. The computational results indicate that the proposed solution procedures are capable of achieving high-quality solutions in reasonable CPU time. These findings suggest that transportation companies could enhance their operational efficiency by implementing similar strategies.
