Generalized gradient algorithms for hybrid system models of manufacturing systems

We study a hybrid system modeling framework for many manufacturing problems. The framework uses event-driven dynamics to describe the movement of jobs through a manufacturing facility. As the jobs are processed by the machines their physical characteristics change according to time-driven dynamics. Algorithms are developed to solve the optimal control problems that arise when one attempts to trade-off demands on job completion times against the quality of the completed jobs. To deal with the nondifferentiabilities associated with the 'max' operation in the event-driven dynamics, generalized gradients are used.