物流网络中包裹分拣中心的数字孪生分拣优化

Parcel distribution centers in logistics networks aim to sort inbound parcels for downstream destinations through parcel-sorting systems, where parcels are diverted to assigned grids and packed into bins for outbound truck deliveries. Considering dynamic parcel movements, random packing behaviors, and conveyor congestion, maximizing sorting throughput by assigning grids to destinations presents significant computational challenges. This work establishes a high-fidelity parcel-sorting digital-twin system that models real-time interactions among parcels, sorters, and packers, and simulates throughput performance under specific sorting plans. To address the computational challenges and real-world operational constraints, a digital twin-based structured Monte Carlo tree search optimization framework, combining integer nonlinear programming and geographic destination graph networks, is proposed to optimize sorting plans. The proposed digital twin and optimization framework have been deployed in 146 parcel distribution centers (approximately 30% of SF Express parcel distribution centers in China) and demonstrated their effectiveness through both numerical and field experiments: the proposed framework increases average and peak sorting throughputs by 10.56% and 7.72%, respectively, and reduces recirculated parcels by 60.81%, compared to field-implemented method. Furthermore, it outperforms the basic digital twin-based Monte Carlo tree search baseline by 6.77% and 4.99% in average and peak throughputs, respectively, and reduces recirculated parcels by 54.57%.