Computational Strategies for Model Predictive Control on Switching Max-plus Linear Systems

A switching max-plus linear model is a framework to describe the discrete dynamics of the timing of events. To influence these systems one can choose the routes of jobs and the orderings of operations as input for the system. In this thesis the techniques of model predictive control are used to find good input values. The problem of finding the...

Automated dig-limit optimization through simulated annealing

Creating an optimal OR schedule regarding downstream resources

A high percentage of hospital admissions is due to surgical interventions. The operating theatre, which holds the operating rooms (ORs), is therefore one of the key resources in hospitals. Managing the operating theatre and finding an optimal OR schedule is complex due to the many factors that influence it. Scheduling a surgery in an OR...

Nonlinear optimization of strategies: finding an optimal race strategy for the TU Delft Solar Boat

The purpose of this thesis was to find a (near-)optimal long-haul race strategy for the TU Delft Solar Boat, taking into account practical race conditions and very limited computing time. A race strategy details at what speeds the boat should sail throughout the race, and how long (if at all) it should stop to charge. Various methods have been...

Optimizing Greenhouse Heat Production in Lansingerland: Using Simulated Annealing and Simulation

Many engineering problems require some objective to be optimized subject to certain constraints. For linear and convex optimization problems, techniques exist that can find the global minimum. For problems that have local minima or complex constraints, finding the global minimum is more difficult. An example of such a problem can be found in...

Improving the description of the SPM concentration in the North Sea using a generic estimation technique for the process controlling parameters

The present thesis aims to improve the current description of the SPM by deriving optimal values for the model’s input parameters. With the use of suitable optimization algorithms, the objective is to capture the spatial variability of the key model parameters.