Operations Research & Optimization

Operations Research

Operations research aims to provide a framework to model complex decision-making problems that arise in engineering, business and analytics, and the mathematical sciences, and investigates methods for analyzing and solving them. The most common solution techniques include mathematical optimization, simulation, queuing theory, Markov decision processes, and data analysis, all of which use mathematical models to describe the system.

Optimization

Optimization aims to find the minimum (or maximum) value of an objective function subject to constraints that represent user preferences and/or limitations imposed by the nature of the question at hand. Research in optimization involves the analysis of such mathematical problems and the design of efficient algorithms for solving them. It is therefore no surprise that optimization, while integral to operations research, has become an indispensable tool in other areas such as statistics, machine learning, computer vision, and computational biology, just to name a few. Optimization technologies are shining examples of how deep mathematical techniques help to provide concrete computational tools for solving a diverse suite of problems. Consequently, the knowledge and experience gained by students who study optimization will make them highly competitive in the job market.

Group in Operations Research and Optimization (GORO)

Faculty in GORO are experts in operations research and various subfields within optimization, such as continuous, discrete, and stochastic optimization. Our research is driven by important applications in areas such as healthcare, astronomy, vision, network modeling, defense systems, and scheduling. This diversity in research involving both theory and application areas, provides varied research questions that allow us to integrate both undergraduate and graduate students into our cutting edge research.

The students and faculty in GORO benefit from various faculty affiliations. These include the Applied Physics Laboratory (APL), the Institute for Computational Medicine (ICM), the JHU Systems Institute, and the JHU Algorithms and Complexity Group. These affiliations and associated collaborations make our group a great example of the truly interdisciplinary nature that is characteristic of Johns Hopkins University.

For more details about our members, research, and course offerings in operations research and optimization, please explore the additional tabs.

 

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