AI-Driven Optimization of Last-Mile Delivery : Reducing Costs and Enhancing Efficiency in Urban Logistics

Abstract

One of the most expensive, complex and environmentally challenging aspects of contemporary logistics has already been last-mile delivery. It actually covers over fifty percent of the total shipping expenses and the effect of its influence on customer satisfaction is enormous. The global e-commerce increases, and the cities become more compact, which puts a strain on the logistics companies like DHL. Such pressure is exacerbated by inflexible routing systems, disintegrated data infrastructures, labour-intensive activities, and random traffic. The thesis examines the application of Artificial Intelligence (AI) to streamline the last-mile delivery process by reducing expenses, increasing efficiency, and being sustainable. The research adopts a quantitative research design, relying on secondary data sources at Kaggle and conducting research on the main operational variables, such as the time of delivery, package count, the experience of the driver, the distance to the city centre, and the environmental factors.