Operations Research



Operations Research aims at the study, development, and application of mathematical, statistical, and computational methods to support decision-making associated with the industry, services, and, more broadly, society. The area encompasses quantitative fundamentals associated with the lines of research in Algorithms and Optimization and Statistical Methods and Analytics.

In addition to methodological aspects, the research developments encompass applications in the areas of Energy, Finance, Logistics and Supply Chain, Health, Telecommunications, among others.

 

Masters Degree in Production Engineering – Click to apply

Doctoral Degree in Production Engineering – Click to apply

The Graduate Program in Production Engineering provides solid expertise in the area of ​​Operations Research, offering research topics and academic opportunities for researchers looking for a career in several areas. An Operations Research professional has broad career opportunities in academia, industry, financial and energy markets, public service, and private sector. In particular, the main challenge for an Operations Research professional is the development and application of predictive and prescriptive techniques, based on data analysis, to support the decision of individuals and organizations.

 

Research line: Algorithms and Optimization

 

Analysis and development of algorithms and optimization techniques applied to production systems and services.

 

Research Projects
Combinatory optimization This projects aims at developing and applying theories, models, methods, analysis and computational tools for combinatory optimization. It encompasses the study of integer programming techniques, relaxations, decompositions, heuristic and meta-heuristic approaches, their combination and extensions towards addressing complex problems on linear programming, convex, non-linear, and stochastic optimizations.
Optimization under uncertainty This project aims at developing and applying theories, models, methods, analysis and computational tools for optimization under uncertainty. It encompasses the study of stochastic programming, robust optimization and distributionally robust optimization and their connections with other themes such as probability theory, simulation and risk measures. 

Research line: Statistical Methods and Analytics

 

Analysis and development of statistical and analytical models applied to production systems and services.

 

Research Projects
Machine Learning This project aims to systematically develop theories, models, methods, analysis, and computational tools for Machine Learning, including classical and advanced models for supervised and unsupervised learning applications.
Time series analysis This project aims to systematically develop and apply theories, models, methods, analysis, and computational tools associated with time series, including classical and advanced models for predicting and simulating time-dependent data and statistical learning methods for traditional statistical approaches. 

 

 

 

 


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