
Ruibin Bai
Director of Lab
Computer Science and Operations Research
Automated Guided Vehicles (AGVs) enhance transportation efficiency in different domains such as warehouses, factories, and container ports. Much research has been done into optimal scheduling and routing of multiple AGVs to improve the overall efficiency of the systems. However, more research efforts are required when addressing more complex real-life systems where the mobility of AGVs is highly constrained due to special geometric shapes and dimensions. Focusing on a real-world hospital AGV routing problem, this paper tackles the additional complexity arising from space capacity constraints long narrow corridors and lifts for cross-floor deliveries. A simulation optimisation approach is introduced to accurately model complex interactions of AGVs under conditions like floor switching, charging, and passing narrow corridors. To tackle the underlining vehicle routing problems with pickup and delivery (VRPPD) which is NP-Hard, this paper presents a simulation-based hyper-heuristic optimization approach to minimize the makespan of all tasks. A surrogate model is integrated to expedite the search process, and several experiments are conducted to properly evaluate the performance of our method. Based on the results, our method exhibits great potential in improving efficiency while maintaining the excellent practicality of AGV routing for complex environments like hospitals.