Our research work is concerned with the development of discrete-continuous optimization models and methods for problems in process systems engineering. We specifically address problems in the areas of process synthesis, planning and scheduling of process systems, through novel mathematical programming approaches, which rely on linear and nonlinear models with discrete and continuous variables. These include mixed-integer programming (MILP and MINLP), General Disjunctive Programming (GDP), global optimizationand multiperiod optimization. Both deterministic models as well as models with uncertainty are considered. Our work also provides a balance between theory, computation and real world applications.

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BatchMPC Design of multiproduct batch plants with mixed product campaigns .	BatchSPC Design of multiproduct batch plants with single product campaigns.
CYCLE Zero-wait scheduling of multiproduct batch plants.	CRUDEOIL Inventory management of a refinery that imports several types of crude oil delivered by different vessels. (under construction)
DECAY Scheduling multiple feeds on parallel units, where the performance of each unit decreases with time.	EXTRACTOR Optimal design of multicomponent liquid-liquid extraction processes using multistage countercurrent extractor systems.
GDP-DISTILL Synthesizing a single distillation column.	LOGMIP Logic Mixed Integer Programming Solver.
MULTISTAGE Optimizing cyclic schedules of multiproduct continuous plants.	NETCHAIN Optimization of the supply chain in continuous flexible process networks.
PRODEV Scheduling of testing tasks in the new product development.	STN State Task Network, continuous and discrete models.
SYNHEAT Program for optimizing heat exchanger networks	WATER Design of a distributed wastewater network.