Optimized Approach to Electric Vehicle Routing Problem with Time Windows Using Grasshopper Optimization Algorithm

The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is a complex logistics issue that involves optimizing delivery routes for electric vehicles while adhering to strict time limits, managing limited battery capacity, and addressing recharging needs. In this research, we introduce an optimized method to tackle the EVRPTW using the Grasshopper Optimization Algorithm (GOA), a metaheuristic inspired by the swarming behavior of grasshoppers. We utilize the Solomon dataset, a recognized benchmark in logistics and vehicle routing, to assess the effectiveness of our proposed algorithm. Our focus is on minimizing the total distance traveled while ensuring timely deliveries and effectively managing battery logistics and recharging. Comparative analysis indicates that the GOA surpasses traditional methods in route efficiency, reducing travel distances, and enhancing logistical operations. This study highlights the potential of GOA as a valuable tool for overcoming the challenges associated with electric vehicle logistics, paving the way for more sustainable and efficient transportation systems.