Destination Coded Vehicles Research Articles

Hierarchical route control in DCV-based baggage handling systems

Modern baggage handling systems transport baggage at high speeds, on track networks, using Destination Coded Vehicles (DCV). To ensure optimal DCVs routing, we propose a hierarchical control framework that consists of local switch controllers providing switch position instructions and a supervisory network controller. The routing control problem for the network controller is a nonlinear optimisation problem with high computational requirements. Therefore, we present an alternative approach for reducing the computational complexity by approximating the nonlinear optimisation and rewriting it as a Mixed Integer Linear Programming (MILP) problem. The advantage is that for MILP problems solvers are available that can efficiently compute the global optimal solution. The MILP solution is then used for computing optimal switch control actions. For a benchmark case study we compare the hierarchical route control with previously developed switch control approaches. Results indicate that the proposed hierarchical control offers a balanced trade-off between optimality and computational effort.

Predictive route control for automated baggage handling systems using mixed-integer linear programming

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 network of tracks. In this paper we consider the problem of controlling the route of each DCV in the system. In general this results in a nonlinear, nonconvex, mixed-integer optimization problem, usually very expensive in terms of computational effort. Therefore, we present an alternative approach for reducing the complexity of the computations by simplifying and approximating the nonlinear optimization problem by a mixed-integer linear programming (MILP) problem. The advantage is that for MILP problems solvers are available that allow us to efficiently compute the global optimal solution. The solution of the MILP problem can then be used as a good initial starting point for the original nonlinear optimization problem. We use model predictive control (MPC) for solving the route choice problem. To assess the performance of the proposed (nonlinear and MILP) formulations of the MPC optimization problem, we consider a benchmark case study, the results being compared for several scenarios.

Model-Based Control for Route Choice in 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 network of tracks. Currently, the DCVs are routed through the system using routing schemes based on preferred routes. These routing schemes respond to the occurrence of predefined events. We do not consider such predefined preferred routes. Instead, we develop advanced control methods to determine the optimal routing in case of dynamic demand. In order to optimize the performance of the system, we first develop and compare efficient centralized, decentralized, and distributed predictive methods. Next, to reduce the computational requirements, we also propose some heuristic methods. Finally, to assess the performance of the proposed control approaches, the methods are compared for several scenarios on a benchmark case study.

Predictive Control for Baggage Handling Systems using Mixed Integer Linear Programming

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 network of tracks. In this paper we consider the problem of controlling the route of each DCV in the system. In general this results in a nonlinear, nonconvex, mixed integer optimization problem which is usually very expensive in terms of computational effort. Therefore, we present an alternative approach for reducing the complexity of the computations by simplifying and approximating the nonlinear optimization problem by a mixed integer linear programming (MILP) problem. The advantage is that for MILP problems solvers are available that allow us to efficiently compute the global optimal solution. The solution of the MILP problem can then be used as a good initial starting point for the original nonlinear optimization problem. We use model predictive control (MPC) for solving the route choice problem. We also assess the performance of the proposed (nonlinear and MILP) formulations of the MPC optimization problem using a benchmark case study.

Decentralized route choice control of automated baggage handling systems

Modern baggage handling processes in airports use destination coded vehicles (DCVs) to transport the luggage at high speeds. These vehicles transport the bags on a “mini” railway network. In order optimize the performance of a DCV-based baggage handling system, the route of each DCV has to be determined. In this paper we consider an event-based model of this system. For routing the DCVs through the network we propose decentralized control methods that independently compute local control actions viz. decentralized model predictive control (MPC) and decentralized heuristic approaches. The considered control methods are compared for several scenarios. Results indicate that decentralized MPC can be used to suboptimally solve the problem. Moreover, the decentralized heuristic approaches usually give worse results than those obtained when using decentralized MPC, but on the other hand they require very low computation time.

Predictive route choice control of destination coded vehicles with mixed integer linear programming optimization

Abstract 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. In this paper we consider the problem of controlling the route of each DCV in the system. This is a nonlinear, nonconvex, mixed integer optimization problem. Nonlinear model predictive control (MPC) for mixed integer problems is usually very expensive in terms of computational effort. Therefore, in this paper we present an alternative approach for reducing the complexity of the computations by simplifying and approximating the nonlinear optimization problem by a mixed integer linear programming (MILP) problem. The advantage is that for MILP optimization problems solvers are available to allow us to efficiently compute the global optimal solution. The solution of the MILP problem can then be used as a good initial starting point for the original nonlinear optimization problem. To assess the performance of the proposed formulation of the MPC optimization problem, we consider a benchmark case study, the results being compared for several scenarios.

Route Choice Control of Automated Baggage Handling Systems

State-of-the-art baggage handling systems transport luggage in an automated way by using destination-coded vehicles (DCVs). These vehicles transport the bags at high speeds on a minirailway network. Currently, the networks are simple, with only a few junctions; otherwise bottlenecks would be created at the junctions. Bottlenecks make the system inefficient. More complex networks were considered. To optimize the performance of the system, centralized and decentralized control methods that could be used to route the DCVs through the track network were developed and compared. The proposed centralized control method is model predictive control (MPC). Because of the large computation effort that centralized MPC required, decentralized MPC and a fast decentralized heuristic approach were also proposed. When the decentralized approaches are implemented, each junction has its own local controller for positioning the switch going into the junction and the switch going out of the junction. To assess the advantages and disadvantages of centralized MPC, decentralized MPC, and the decentralized heuristic approach, a simple benchmark case study was studied. The considered control methods were compared for several scenarios. Centralized MPC becomes intractable when a large stream of bags must be handled, whereas decentralized MPC can still be used to solve the problem suboptimally. Moreover, the decentralized heuristic approach usually gives worse results than those obtained when using decentralized MPC but with very low computation time.