Optimizing capacitated electric vehicle route in logistic operations - Tiong, Samuel Fu Wei (2024)
As the use of eco-friendly practices in logistics grows, optimizing routes for CEVs remains challenging due to their limited energy and load capacities. This project aims to develop an efficient route optimization algorithm for Capacitated Electric Vehicles (CEVs) in logistics, focusing on minimizing travel distance while adhering to vehicle capacity and battery limitations. To address this, the Ant Colony Optimization (ACO) technique was chosen for its ability to efficiently explore large solution spaces and identify optimal routes. The algorithm’s performance was further enhanced by applying the Taguchi method to fine-tune key parameters to solve the Capacitated Electric Vehicle Routing Problem (CEVRP). Key parameters, such as the number of ants, pheromone influence, heuristic information, and evaporation rate, were optimized using the Taguchi method. The analysis showed that these parameters significantly impacted the route optimization, leading to a reduction in total travel distance. The results demonstrated that the optimized algorithm effectively minimized the distance traveled by CEVs while meeting operational constraints. This approach not only improves the efficiency of logistics operations but also contributes to sustainable transportation, making it applicable across various logistics and supply chain scenarios.