ALNS Framework for Platform-Based Vehicle Scheduling in Logistics Industrial Park CPSs

The limitation of platform resources is usually the bottleneck of loading/unloading operations in logistics. Hence, the strategies for utilizing platforms significantly impact the efficiency of logistics, which further affects the throughput of logistics industrial parks. In order to improve the efficiency of logistics management and operation, platform-based vehicle scheduling is studied in this work, which aims to appropriately assign the vehicles for loading/unloading to specific platforms and time ranges, thereby optimizing the usage of platforms and avoiding unnecessary waiting. The recent trend of Industrial 4.0 has further promoted the applications of the cyber-physical system (CPS) infrastructure in modern logistics systems, allowing the scheduling based on the real-time states of entities and resources. However, this also poses new challenges such that the decisions to support large-scale scheduling tasks need to be made within limited time. In order to tackle these challenges and enable real-time decision making, this work proposes an innovative scheduling algorithm based on the adaptive large neighborhood search (ALNS) framework to optimize the operation time in the logistics industrial park with high efficiency. The proposed algorithm is compared against the Gurobi professional solver based on real-world data for vehicle scheduling in logistics indus-trial parks, where it is demonstrated to significantly outperform Gurobi on the efficiency and scalability in both day-ahead and real-time scenarios without any degradation of solution quality.