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Since 1968, when the doctoral program in operations research was started, the Tepper School has initiated several new developments in integer and nonconvex programming, enumerative methods, cutting plane theory, disjunctive programming, constraint programming, network design, algorithm design, machine learning, data mining, and scheduling models. Geometric programming, chance constrained programming, and the applications of linear programming to capital budgeting and cost management were among the accomplishments of the '50s and early '60s. Our HistoryĬarnegie Mellon has pioneered several important developments in both theoretical and applied operations research. For this reason, student working papers written in collaboration with a faculty member are common.
#Phd in operational research professional#
dissertation, which can begin as soon as the student has passed the third-semester qualifying examination.Īlmost invariably, by the end of their second year, if not earlier, students have already worked on professional problems with some of the faculty. In many cases, work on these papers leads to the work on the Ph.D. Easy interaction in the Tepper School with researchers in the other areas of business and economics and in such related areas as computer science, machine learning, and statistics encourages the application of operations research in imaginative new directions. The paper may be done individually or jointly with other students or faculty members. The research papers assigned for the first and second summers of graduate study are designed to give students an early introduction to research work. The third semester competence examination is based on the areas covered in these courses. Since classes are usually small, students frequently meet informally with their instructors. Each course is taught by a faculty member who is actively pursuing research in the subject area. The basic operations research courses offered include: linear, nonlinear, integer and dynamic programming graph theory and network optimization convex optimization and convex analysis and stochastic models. There is a rich tradition of graduates from the program going on to successful careers in these areas both in academia (in business schools, engineering schools in IE and OR departments as well as in Math and Computer Science departments) and industry. Towards this goal, the program provides the opportunity to develop knowledge of functional areas of business to which optimization can be applied such as Marketing, Operations and Finance.