Logistics Optimization Models Improving Last-Mile Delivery Performance and Reducing Operational Costs for Global Companies

Last-mile delivery represents a critical component of global supply chains, directly influencing operational costs, customer satisfaction, and competitive advantage. As e-commerce and international trade continue to expand, companies face increasing pressure to optimize last-mile logistics to ensure timely, reliable, and cost-efficient deliveries. This examines logistics optimization models designed to enhance last-mile delivery performance while reducing operational expenses for global companies. Optimization approaches range from mathematical and algorithmic models, such as Vehicle Routing Problem (VRP) formulations and MixedInteger Linear Programming (MILP), to heuristic and metaheuristic techniques, including genetic algorithms, ant colony optimization, and hybrid methods. These models enable firms to plan efficient routes, allocate resources effectively, and adapt to dynamic demand patterns. Advancements in data-driven and AI-enabled approaches further enhance last-mile logistics by incorporating predictive analytics, IoT-enabled real-time tracking, and machine learning-based demand forecasting. Such tools allow for adaptive routing, proactive risk management, and realtime operational adjustments, thereby reducing fuel consumption, labor costs, and delivery time variability. Case studies across e-commerce, retail, manufacturing, and courier service sectors demonstrate that optimized last-mile logistics not only improve delivery speed and reliability but also enhance customer experience and brand reputation. Despite these advantages, challenges persist, including the complexity of global multi-modal networks, demand uncertainty, regulatory constraints, and technological investment requirements. Addressing these barriers requires integrated strategies combining algorithmic optimization, supplier and carrier collaboration, and digital monitoring platforms. Logistics optimization models serve as a strategic enabler for global companies, improving operational efficiency, reducing costs, and enhancing competitive positioning. By leveraging advanced optimization techniques, AI, and IoT, organizations can achieve sustainable, agile, and resilient last-mile delivery networks. This research underscores the importance of integrating technology, analytics, and strategic planning to meet the evolving demands of global supply chains, ultimately driving long-term performance and value creation.