AGV Scheduling for Optimizing Irregular Air Cargo Containers Handling at Airport Transshipment Centers

Abstract

Airport transshipment centers play a pivotal role in global logistics networks, enabling the swift and efficient transfer of cargo, which is essential for maintaining supply-chain continuity and reducing delivery times. The handling of irregularly shaped air cargo containers presents new constraints for automated guided vehicles (AGVs), as these shapes can complicate loading and unloading processes, directly impacting overall operational efficiency, turnaround times, and the reliability of cargo handling. This study focuses on optimizing the scheduling of AGVs to enhance cargo-handling efficiency at these hubs, particularly for managing irregular air cargo containers. A mixed-integer linear programming (MILP) model is developed, validated for feasibility with the Gurobi solver, and designed to handle large-scale operations. It incorporates a novel approach by integrating a simulated annealing optimized genetic algorithm (GA). The experimental results demonstrate that the designed algorithm can solve models of considerable size within 8 s, offering superior time efficiency compared to the solver, and an average solution quality improvement of 12.62% over the genetic algorithm, significantly enhancing both the model’s efficiency and scalability. The enhanced AGV scheduling not only boosts operational efficiency but also ensures better integration within the global logistics framework. This research provides a robust foundation for future advancements in logistics technology, offering both theoretical and practical insights into optimizing complex transportation networks.