Ricardez-Sandoval, LuisValdez Navarro, Yael Izamal2019-12-042019-12-042019-12-042019-11-25http://hdl.handle.net/10012/15276This thesis presents a decomposition algorithm for obtaining robust scheduling and control decisions. It iteratively solves scheduling and dynamic optimization problems while approximating stochastic uncertainty through back-off terms, calculated through dynamic simulations of the process. This algorithm is compared, both in solution quality and performance, against a fully-integrated MINLP.enoptimizationschedulingcontroluncertaintyCombinatorial optimizationMaster Thesis