Operations Research and Optimization

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 focuses on finding 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 provide 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.