A TWO-LEVEL MULTI-OBJECTIVE MODEL AND SIMULATED ANNEALING METAHEURISTIC FOR COST, CO2 EMISSION, AND CUSTOMER SATISFACTION OPTIMIZATION IN LAST-MILE DELIVERY

Abstract

ABSTRACT Last-mile delivery-the final and most critical stage of fulfilling customer orders-is typically the most costly and time-consuming component of the distribution process. Optimizing this stage is therefore essential for improving the overall efficiency and sustainability of logistics operations. This study begins with a comprehensive review of the principal contributions to last-mile delivery optimization. Building on insights from existing models and metaheuristics, we propose a new two-level framework in which customers are served through distribution centers, ensuring that each customer location lies within a designated coverage area. The framework consists of two interconnected levels: depot to distribution center routing and distribution center to customer delivery. To support this structure, we develop a new optimization model and a Simulated Annealing-based metaheuristic inspired by the vehicle routing problem with time windows and tailored specifically to last-mile customer service operations. Computational results demonstrate that the proposed approach provides both economic benefits-through reduced travel distance-and environmental advantages through lower CO2 emissions. Moreover, the routing plan generated at the second level allows for the quantification of customer satisfaction-averaging 72.5% for the optimization model and 100% for the metaheuristic-thereby providing a more comprehensive and robust evaluation of delivery.