Requirements for a Ph.D. in Operations Research
The requirements for the Ph.D. in Operations Research include:
- Course work
- Third semester qualifying examinations
- First and second year papers
- Ph.D. dissertation
A normal course load involves taking 120 units during the first two years, including core Operations Research courses and a minor area of concentration. Courses that run over a mini are worth 6 units.
The purpose of the minor requirement is to broaden the students’ knowledge to make them capable of teaching a wider range of courses and to enhance their research capabilities. The needs and preferences of individuals are recognized; therefore, the courses they take can vary. Examples of areas of concentration for the minor requirement include:
- Statistics
- Computer Science
- Operations Management
- Finance
- Marketing
- Machine Learning
At least 18 units should be taken in the minor area. Students with appropriate preparation prior to their entry to the program may take the qualifying exams prior to the third semester point if they choose, but they must take the entire set of qualifiers in the Operations Research area of study. The qualifying exams consist of eight questions, five of which are mandatory while the other three are chosen from a list of six.
Core Courses
- Linear Programming
- Network Optimization I
- Integer Programming
- Stochastic Processes (e.g. Performance Modeling)
- Modern Convex Optimization
Plus three from the following list:
- Network Optimization II
- Convex Analysis
- Constraint Programming
- Advanced Linear Programming
- Advanced Integer Programming
- Nonlinear Programming
Additional Electives in Operations Research at the Tepper School
- Combinatorial Optimization
- Convex Polytopes
- Dynamic Programming
- Open Source Software for Optimization
- Social, Economic and Information Networks
Students are encouraged to also take electives in other departments on campus, such as Computer Science or Mathematical Sciences.
Example of a Course Sequence
Year 1
M1: Linear Programming, Network Optimization I, Performance Modeling
M2: Network Optimization II OR Advanced LP, Constraint Programming OR Convex Analysis, Performance Modeling
M3: Integer Programming, Convex Optimization, course in minor area
M4: Advanced IP, Nonlinear Programming, course in minor area
Year 2
M1: Elective, course in minor area
M2: Network Optimization II OR Advanced LP, Constraint Programming OR Convex Analysis
M3: Elective, course in minor area
M4: Elective, elective