Optimizing vehicle selection in supply chain management with data-driven strategies

Abstract

Logistics has undergone significant transformation to address the complex economic, social, and environmental challenges of the modern era. To maintain competitiveness, logistics providers have been compelled to optimize operations, meet increasing customer expectations, and improve satisfaction. Critical issues impacting logistics performance include traffic congestion, infrastructure limitations, rising demand, and the complexities of vehicle scheduling, coordination, and management. These challenges frequently disrupt delivery operations, undermining efficiency and overall system performance. This paper proposes the application of three machine learning models aimed at optimizing delivery processes, with a focus on improving vehicle assignment for order deliveries. By leveraging these models, logistics providers can enhance decision-making and operational efficiency. The study defines the core problem and evaluates several machine learning approaches to bolster logistics delivery systems.