Multi-Route Collaborative Optimization for Integrated Vehicle-Crew Scheduling in Electric Airport Shuttle Bus Operations under Flight-Induced Service Pressure and Time-Dependent Speeds

Airport shuttle bus operations are strongly affected by imbalanced directional demand, heterogeneous routes, electric bus charging requirements, and flight-induced service pressure. This paper investigates an integrated vehicle-crew scheduling problem for electric airport shuttle buses with multi-route coordination, time-dependent travel speeds, and flexible service start times. A flight-induced service pressure index (SPI) is developed to measure the temporal concentration of flight arrivals and departures and to define trip-specific time-window flexibility. A mixed-integer linear programming model is formulated to jointly optimize vehicle schedules, charging decisions, service start times, and driver duties. The model considers trip coverage, battery feasibility, vehicle-driver synchronization, working-time limits, and workload-balance requirements. To solve large-scale instances, a two-stage variable neighborhood search algorithm is designed, in which electric bus paths, charging decisions, and service start-time adjustments are first optimized, and driver duties are then constructed and improved. Computational experiments are conducted on instances derived from real-world shuttle bus operations at Xianyang International Airport. The results show that the valid inequalities reduce the mean CPLEX solution time from 107.70 s to 25.46 s. The proposed algorithm achieves an average objective gap of 2.08% relative to CPLEX on small-scale instances. It also reduces the objective value by 2.90% compared with ALNS on large-scale instances. The results further show that multi-route collaboration reduces operating costs and balances workloads, while SPI-based time windows enhance service stability during flight-intensive periods.