A Hybrid Periodic and Event-Driven Rolling Horizon Optimization Approach for Airport Logistics Vehicle Scheduling

The efficient scheduling of airport logistics vehicles is crucial for ensuring timely and cost-effective ground operations, particularly under dynamic disturbances such as flight delays, cancellations, and new task arrivals. With the increasing deployment of Internet of Things (IoT) technologies in airport environments, real-time data from sensors and connected devices enables efficient and adaptive scheduling. This paper considers a dynamic Airport Logistics Vehicle Scheduling (ALVS) problem that aims to minimize both vehicle usage and total task waiting time while satisfying task precedence and time window constraints. To address this problem, we propose a hybrid optimization framework, termed Periodic and Event-Driven Rolling Horizon Optimization (PERHO), which integrates periodic updates with event-driven rescheduling to adapt to real-time task variations in airport ground operations. Within PERHO, an Order-aware Adaptive Strategy Selection (OASS) algorithm is developed to dynamically select the most appropriate task sequencing heuristic from a candidate set based on recent performance and order relationships. Extensive experiments across various instance scales and dynamic scenarios demonstrate the effectiveness of the proposed PERHO-OASS approach. In experiments considering dynamic events, PERHO-OASS reduces vehicle usage and task waiting time by an average of 23.55% and 61.95%, respectively, over fixed heuristic algorithms, and by an average of 3.77% and 17.30% over adaptive selection methods, demonstrating strong robustness under uncertainty. The proposed approach can support airport operators in improving the efficiency and reliability of ground logistics operations.