Vehicle Routing Problem with Time Window for Disruption Management using Genetic Algorithm

Abstract

The Vehicle Routing Problem with Time Windows (VRPTW) is a critical logistics challenge that involves planning delivery routes to meet customer demands within specific time constraints while minimizing costs. Real-world disruptions, such as vehicle breakdowns and road blockages, can significantly affect delivery efficiency and operational costs. Addressing these challenges requires robust optimization methods. This study aims to solve the VRPTW by utilizing a Genetic Algorithm (GA) to optimize delivery routes under different disruption scenarios. The GA dynamically adjusted routes to handle disruptions while adhering to constraints such as vehicle capacity and time windows. The algorithm was implemented in Python, and three scenarios were analysed. The first scenario is a baseline scenario with no disruptions, where the total cost was RM652.62 while the second scenario is a road blockage scenario, where the GA rerouted deliveries and resulted in a cost of RM712.02 and the last scenario is a vehicle breakdown scenario, where tasks were reassigned to other vehicles, resulting in a cost of RM805.24.The results demonstrate the effectiveness of the GA in reducing costs, managing disruptions, and improving delivery performance. This research highlights the potential of optimization algorithms to enhance delivery operations, offering cost savings and improved efficiency in real-world logistics.