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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. |
