Forecasting Route Stability in Urban Delivery Fleets with Intelligent Optimization Methods

Abstract

The exponential growth of e-commerce has significantly intensified the operational demands placed on urban delivery fleets, necessitating advanced approaches to last-mile logistics. Traditional routing optimization predominantly focuses on minimizing operational costs or travel distances, often neglecting the crucial aspect of route stability. Route stability, defined as the consistency of delivery routes and schedules over consecutive operational periods, plays a pivotal role in driver familiarity, customer satisfaction, and overall service reliability. This paper investigates the predictability and management of route stability using intelligent optimization methods within a comprehensive simulation environment tailored for urban delivery fleets. By employing an advanced agent-based simulation framework, we evaluate the performance of various heuristic and metaheuristic algorithms in balancing the inherent trade-offs between dynamic cost optimization and spatial-temporal route consistency. The simulation study incorporates highly realistic urban logistical constraints, including stochastic traffic congestion patterns, variable customer demand, and strict delivery time windows. The findings demonstrate that intelligent optimization strategies incorporating specific stability penalty functions can effectively predict and maintain high levels of route consistency without severely compromising operational efficiency. Furthermore, the analysis reveals that maintaining route stability significantly mitigates the disruptive impacts of localized traffic anomalies. This research provides substantial theoretical contributions to the field of dynamic vehicle routing and offers practical insights for fleet managers seeking to enhance service reliability and workforce satisfaction in complex urban environments.