Airport baggage route planning and scheduling optimization based on digital twin technology

With the rapid development of the aviation industry, the efficiency and reliability of airport baggage handling systems are crucial for airline operational efficiency and passenger experience. To address the increasingly complex baggage handling demands, this paper proposes a method combining genetic algorithms (GA) for multi-objective path planning and scheduling optimization to solve the airport baggage transportation process's path selection and resource scheduling problems. By constructing a digital twin model, the system monitors the real-time status of equipment, path congestion, vehicle loads, and other information to support path planning. The genetic algorithm optimizes path planning by considering multiple objectives, including transportation time, cost, equipment utilization, and carbon emissions. Experimental results show that this method effectively reduces transportation time and costs, improves equipment utilization, and optimizes carbon emissions. Through sensitivity analysis, we found that population size and mutation probability significantly impact the optimization results. Compared to other algorithms (such as MOEA/D), the genetic algorithm shows clear advantages in convergence, solutions diversity, and Pareto front optimization. This study provides theoretical support for the intelligent optimization of airport baggage handling systems and looks forward to future research directions that integrate multi-objective optimization and intelligent scheduling.