Hybrid Algorithm for Large Scale in Electric Vehicle Routing and Scheduling Optimization

Abstract

This study examines the optimal scheduling and routing of electric vehicles across multiple time periods to reduce battery consumption and commute times. The objective is to convey products to consumers using a fleet of electric vehicles as efficiently as feasible. This hybrid solution addresses this issue and combines the most advantageous aspects of the Local Search 2-Opt and Particle Swarm Optimization (PSO) metaheuristics. Local Search 2-Opt is used to enhance the quality of the original solutions, whereas PSO is used to search through all possible solutions and identify the best one. Considering constraints such as limited vehicle capacity, limited charging station availability, and limited time, the proposed method optimizes the coupling of consumers with electric vehicles. The evaluation results indicate that the hybrid algorithm accomplishes more significant energy savings, travel time savings, and overall solution quality than the alternatives currently considered to be state-of-the-art. This research's findings can be used to optimize EVRS operations and improve the administration of electric vehicle fleets.