Optimal Routing of Electric Vehicles with Simultaneous Delivery and Pickup Using Metaheuristic Methods

This study addresses the Electric Vehicle Routing Problem (EVRP) with simultaneous delivery and pickup, integrating operational constraints, customer demands, vehicle capacity limits, and energy management considerations. A comprehensive mathematical model is developed with the primary objective of minimizing the total traveled distance while enhancing the efficiency and sustainability of urban transportation systems. The model incorporates key factors such as optimal charging station placement, time window constraints, and real-time decision-making for delivery and pickup operations. To solve this complex problem, both exact optimization techniques (using CPLEX) and metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bat Algorithm, are employed. Comparative analysis reveals that the proposed metaheuristics significantly reduce computation time while producing high-quality solutions close to the optimal values obtained from CPLEX. Moreover, the algorithms demonstrate robust performance under varying traffic conditions and demand uncertainties.

Keywords:

Electric Vehicle Routing Problem (EVRP) , Simultaneous Delivery and Pickup , Metaheuristic Algorithms , Sustainable Transportation , Optimization