DELIVERY ROUTES OPTIMIZATION USING MACHINE LEARNING ALGORITHMS

Abstract

Delivery route optimization is a crucial concern in the logistics industry, affecting delivery times, costs, and customer satisfaction. The conventional methods for optimizing delivery routes are time-consuming and require substantial manual efforts. To address these limitations, they have increasingly used machine learning algorithms for more efficient and effective optimization. This paper reviews modern techniques for delivery route optimization using machine learning algorithms, including the key challenges faced by delivery companies. Metaheuristic methods, reinforcement learning, and machine learning are discussed, along with their advantages and limitations. In developing a delivery route optimization system, factors such as the number of vehicles, their capacity, delivery time windows, road networks, and customer demand are considered. Different optimization objectives, such as minimizing delivery time, reducing transportation costs, and maximizing customer satisfaction, are presented. Finally, the paper highlights future research directions, including multi-agent systems, swarm intelligence, and hybrid algorithms. This paper provides a comprehensive review of delivery route optimization using machine learning algorithms and can be useful for practitioners and researchers in the logistics industry.