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

Abstract

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.