Model predictive scheduling of semi-cyclic discrete-event systems using switching max-plus linear models and dynamic graphs

In this paper we discuss scheduling of semi-cyclic discrete-event systems, for which the set of operations may vary over a limited set of possible sequences of operations. We introduce a unified modeling framework in which different types of semi-cyclic discrete-event systems can be described by switching max-plus linear (SMPL) models. We use a dynamic graph to visualize the evolution of an SMPL system over a certain period in a graphical way and to describe the order relations of the system events. We show that the dynamic graph can be used to analyse the structural properties of the system. In general the model predictive scheduling design problem for SMPL systems can be recast as a mixed integer linear programming (MILP) problem. In order to reduce the number of optimization parameters we introduce a novel reparametrization of the MILP problem. This may lead to a decrease in computational complexity.</p

Model predictive scheduling of semi-cyclic discrete-event systems using switching max-plus linear models and dynamic graphs

In this paper we discuss scheduling of semi-cyclic discrete-event systems, for which the set of operations may vary over a limited set of possible sequences of operations. We introduce a unified modeling framework in which different types of semi-cyclic discrete-event systems can be described by switching max-plus linear (SMPL) models. We use a dynamic graph to visualize the evolution of an SMPL system over a certain period in a graphical way and to describe the order relations of the system events. We show that the dynamic graph can be used to analyse the structural properties of the system. In general the model predictive scheduling design problem for SMPL systems can be recast as a mixed integer linear programming (MILP) problem. In order to reduce the number of optimization parameters we introduce a novel reparametrization of the MILP problem. This may lead to a decrease in computational complexity.Team DeSchutterDelft Center for Systems and Contro

Scheduling Inland Waterway Transport Vessels and Locks Using a Switching Max-Plus-Linear Systems Approach

This paper considers the inland waterborne transport (IWT) problem, and presents a scheduling approach for inland vessels and locks to generate optimal vessel and lock timetables. The scheduling strategy is designed in the switching max-plus-linear (SMPL) systems framework, as these are characterized by a number of features that make them well suited to represent the IWT problem. In particular, the resulting model is linear in the max-plus algebra, and SMPL systems can switch between modes, an interesting feature due to the presence of vessel routing and ordering constraints in the model. Moreover, SMPL systems can be transformed into mixed-integer linear programming (MILP) problems, for which efficient solvers are available. Finally, a realistic case study is used to test the approach and assess its effectiveness.</p

Online identification of continuous bimodal and trimodal piecewise affine systems

This paper investigates the identification of continuous piecewise affine systems in state space form with jointly unknown partition and subsystem matrices. The partition of the system is generated by the so-called centers. By representing continuous piecewise affine systems in the max-form and using a recursive Gauss-Newton algorithm for a suitable cost function, we derive adaptive laws to online estimate parameters including both subsystem matrices and centers. The effectiveness of the proposed approach is demonstrated with a numerical example.Accepted Author ManuscriptTeam DeSchutte

Receding-horizon control for max-plus linear systems with discrete actions using optimistic planning

This paper addresses the infinite-horizon optimal control problem for max-plus linear systems where the considered objective function is a sum of discounted stage costs over an infinite horizon. The minimization problem of the cost function is equivalently transformed into a maximization problem of a reward function. The resulting optimal control problem is solved based on an optimistic planning algorithm. The control variables are the increments of system inputs and the action space is discretized as a finite set. Given a finite computational budget, a control sequence is returned by the optimistic planning algorithm. The first control action or a subsequence of the returned control sequence is applied to the system and then a receding-horizon scheme is adopted. The proposed optimistic planning approach allows us to limit the computational budget and also yields a characterization of the level of near-optimality of the resulting solution. The effectiveness of the approach is illustrated with a numerical example. The results show that the optimistic planning approach results in a lower tracking error compared with a finite-horizon approach when a subsequence of the returned control sequence is applied.Accepted Author ManuscriptTeam DeSchutte