Receding horizon approaches for route choice control of automated baggage handling systems

State-of-the-art baggage handling systems transport luggage in an automated way using destination coded vehicles (DCVs). These vehicles transport the bags at high speeds on a “mini” railway network. Currently, the networks are simple and the performance of the system is limited. In the research we conduct, more complex networks are considered. In order to optimize the performance of the system we compare several predictive control methods that can be used to route the DCVs through the track network. More specifically, we consider centralized, decentralized, and distributed model predictive control (MPC). To assess the performance of the proposed control approaches, we consider a simple benchmark case study, in which the methods are compared for several scenarios. The results indicate that the best performance of the system is obtained when using centralized MPC. However, centralized MPC becomes intractable when the number of junctions is large due to the high computational effort this method requires. Decentralized and distributed MPC offer a balanced trade-off between computation time and optimality.