The research carried out in the Graduate Program in Systems Engineering is the core component of the program. Faculty and students are actively involved in academic and scientific activities: seminars, attendance at foreign and domestic and conferences, scientific research projects, and innovation projects with industry, among others. Three main knowledge and application research directions (LGACs) are engaged: Stochastic Processes and Simulation Systems, Advanced Optimization Methods, and Industrial Systems Optimization.
The LGAC “Stochastic Processes and Simulation Systems” focuses on the study of systems whose parameters have a significant variability, behaving as random variables. In this case, these parameters or random variables are modeled in probabilistic terms and certain conditions on the probability functions describing their behavior is assumed. This line of research focuses on the analysis, study, and development of policies that provide effective solutions to the problem in question within a framework of uncertainty, in other words, provide necessary scientific tools to quantify the possible decisions by taking into account the random nature of the system parameters. As in deterministic systems, mathematical modelling of the problem is a cornerstone for further analysis and study. The difference is that in trying to develop solution techniques, the concept of “optimal solution” is somewhat inaccurate given the uncertainty condition of the system data. In this case, instead of an optimal solution, one speaks of developing a frame or range of decisions or policies that support quantitatively the decision making process. Among the sub-fields derived from this LGAC are: applied probability models, stochastic processes, discrete event simulation, reliability of systems subject to failures, and stochastic optimization, as well as their application to problems arising from the service, manufacturing, transportation, and energy industry, to mention a few.
The LGAC “Advanced Optimization Methods” focuses on addressing mainly deterministic systems and includes the derivation of mathematical models and development of mathematical and computational techniques to address complex optimization and decision making problems. These include the development and implementation of state-of-the-art computational methods such as exact and heuristic algorithms. Related sub-fields considering in this LGAC are: network flow programming, integer programming, combinatorial optimization, nonlinear programming, dynamic programming, scheduling optimization, and location science, to name a few.
The LGAC “Industrial Systems Optimization” includes the application of the tools of systems engineering and operations research to tangible problems from industry. Among the application areas addressed in this line are traditional problems from service, manufacturing, chemical engineering, telecommunications, transportation, and logistics, and novel applications from use of natural resources and environment, forestry management, bio-medicine, and healthcare management, to name a few.