PPGEP/PUC-Rio conducts its research in two areas of concentration:
Operations and Business Engineering
Operations and Business Engineering aims at the study, development, and application of Production Engineering techniques for operations and business management. The area addresses research problems on Operations Management and Business Management.
The research focuses on improving decision-making and fostering organization productivity through the combination of methodological, technological, social, and environmental aspects. Application areas include Energy, Finance, Logistics and Supply Chain, Health, Telecommunications, among others.
The Graduate Program in Production Engineering provides a solid background in Operations and Business Engineering, offering research topics and academic opportunities for researchers from different areas. Training in this area offers career opportunities in academia, industry, the service sector, public service, and the private market.
In particular, research in this area addresses performance improvement of organizations at both the operational and the strategic levels, including industrial and service operations, with applications in various sectors, such as Digital Health, Oil and Renewable Energy, Finance, Manufacturing, Sustainability, Humanitarian Operations, Logistics, and Supply Chain Management. Research topics include risk analysis, decision support, humanitarian logistics, innovation and sustainability, supply chain integration, business process management, conceptual modeling, data science and analytics, cognitive computing, digital transformation and Industry 4.0, and energy planning.
Research Line: Operations Management
Conception, design, analysis and development of strategic, tactical and operational issues for planning, programming and controlling service and productive systems.
| Research Projects | |
|---|---|
| Supply Chain Management | Application of quantitative and qualitative tools to improve Supply Chain Management, particularly its integration and transition towards the development of new products and services considering the impact of Digital Transformation and of new processes addressing economic, social, and environmental sustainability and planning humanitarian operations in disastrous situations. |
| Productive Systems and Logistics | Application of quantitative and qualitative tools to improve the planning, execution, and performance management of productive and logistics systems including industrial and service operations, production planning and control, stock management, sustainable logistics, and humanitarian logistics in disastrous situations, in both public and private sectors. |
Research Line: Business Management
Specification, analysis, and development of conceptual models, analytical techniques, and methodologies to support decision-making and organizational risk management, business processes management, and digital integration of production systems and services.
| Research Projects | |
|---|---|
| Decision Analysis | This project focuses on the development, integration, and innovative application of theories, models, methods, algorithms, techniques, and technologies for supporting decision-making processes. These methods encompass the analysis of decisions under uncertainty; risk management; experimental and descriptive studies about the behavior of decision-makers; economic analysis of strategic decisions and investment decisions; support for group decision-making; |
| Business Analytics | This project focuses on the development, integration, and innovative application of theories, models, methods, algorithms, techniques, and technologies for data analysis to support business decisions and the end-to-end analytical process. These analyses are considered a complete process for solving business issues, encompassing a broad set of analytical methodologies to foster value creation. Such technologies may represent innovations in specific steps and/or in the execution of the whole process, aiming to impact business. |
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.
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. |