Adaptive large neighborhood search for airport baggage transportation and worker allocation

With the continued growth of air traffic, airports are under increasing pressure to enhance baggage transportation efficiency and operational coordination. As a critical component of ground handling services, the performance of baggage transport directly affects flight punctuality and operating costs. This is especially vital during peak periods of flight arrivals and departures, where improving service efficiency is essential for responding to disruptions and maintaining operational stability. This study addresses the airport baggage transportation and worker allocation problem by developing a mathematical model that incorporates multiple realistic constraints, including time windows, multiple trips, simultaneous pickup and delivery, and demand splitting. The model explicitly considers the impact of the number of workers on service efficiency. To improve dolly utilization, the number of dispatched dollies is incorporated into the objective function, along with the weighted service delay, fixed and fuel costs of tugs, and worker wages. An adaptive large neighborhood search metaheuristic is proposed to solve the problem, featuring seven customized operators and a task reallocation mechanism to improve the algorithm’s ability to escape local optima. In small-scale experiments, comparisons with the CPLEX solver demonstrate the algorithm’s effectiveness. In large-scale instances, the proposed method exhibits strong stability, achieving an average relative gap of less than 6%. Experimental results validate that the proposed model and solution approach significantly improve resource allocation efficiency and reduce operational costs, offering an effective decision-support tool for collaborative optimization in airport baggage transportation services.