Abstract
This chapter presents a unified and extended account of previous work regarding modern baggage handling systems that transport luggage in an automated way using destination-coded vehicles (DCVs). These vehicles transport the bags at high speeds on a network of tracks. To control the route of each DCV in the system we first propose centralized and distributed predictive control methods. This results in nonlinear, nonconvex, mixed integer optimization problems. Therefore, the proposed approaches will be expensive in terms of computational effort. As an alternative, we also propose a hierarchical control framework where at higher control levels we reduce 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. To compare the performance of the proposed control approaches we assess the trade-off between optimality and CPU time for the obtained results on a benchmark case study.
Published Version
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