HomeOverviewAcademicsResearchPeopleStudent ResourcesSeminars & EventsCareer Center
Operations Research Center
Academics

General Degree Requirements

SM Program

PhD Program

 - PhD Tracks

Subject Offerings

Funding Resources

Admission

Open CourseWare

UROP Opportunities

The Doctoral Program

The doctoral degree program in operations research enables PhD candidates to contribute to advanced research and state-of-the-art knowledge in a selected field. The program provides both a thorough background in the theory of OR as well as in developing and applying OR methods in practice.

Many graduates of the doctoral degree program assume academic positions in the US and abroad; others work usually as researchers or consultants in business and industry.

The emphasis of the doctoral program is on dissertation research. The formal course work and other requirements provide a foundation for the student to undertake original and creative research in a topic area selected in consultation with a thesis advisor. A student may wish to focus solely on the theory and methodology of operations research or, alternatively, on the application of OR methods and models in a particular area.

The following requirements are for the ORC's general degree track. In addition to thistrack the ORC offers two optional tracks specializing in the areas of Operations Management and Networked Systems. For more information on the specific requirements for each of these tracks, please see our PhD Tracks page.

Course Requirements

  • Students must take at least eight graduate level classes. Students need to have their classes approved by the ORC co-directors.

  • At least two of these subjects must be taken in optimization from the list below, at least two in applied probability, at least one in statistics and at least one in OR modeling.

  • These subjects must be taken by the end of the student's 6th semester and students must maintain a GPA of 4.5 or better.

Optimization Subjects:

  • 6.251J/15.081J Introduction to Mathematical Programming
  • 6.252J/15.084J Nonlinear Programming
  • 6.855J/15.082J Network Optimization
  • 6.859J/15.083J Integer Programming and Combinatorial Optimization Applied
  • 15.094J/1.142J Robust Modeling, Optimization, and Computation

Probability Subjects:

  • 6.262 Discrete Stochastic Processes
  • 6.264J/15.072J Queues: Theory and Application
  • 6.436/15.085J Fundamentals of Probability
  • 15.070 Advanced Stochastic Processes

Statistics Subjects:

  • 6.437 Inference and Information
  • 6.438 Algorithms for Inference
  • 6.867 Machine Learning
  • 9.520 Statistical Learning Theory and Applications
  • 14.382 Econometrics
  • 15.077J/ESD.753J Statistical Learning and Data Mining
  • 15.097 Prediction: Machine Learning and Statistics * (for S’12 only)

OR Modeling Subjects:

  • 1.203J/15.073J, etc. Logistical and Transportation Planning Methods
  • 1.270J/ESD.273J Logistics and Supply Chain Management
  • 6.254 Game Theory with Engineering Applications
  • 6.268 Network Science and Models
  • 15.071 The Analytics Edge
  • 15.764.1 Inventory Theory and Supply Chains
  • 15.764.2 Revenue Management and Pricing

Hands-on-experience – Doctoral degree student must satisfy a Hands-on-Experience requirement. This requirement can be satisfied in three ways, namely:

Option 1: The student engages in a summer internship during which the student builds OR models that address a real-world application. The student submits a) a brief one-two page report that outlines what has been done (to be submitted electronically via email), and b) a letter, from the internship supervisor, outlining the extent to which OR models have been used to address a real-world application and the student's role in it.

Option 2: Undertake a "Hands-On" project with an ORC faculty member, either as part of a supervised research activity or an extra part of a regular subject. The student and the faculty member should submit documentation of the project and the work he/she has performed.

Option 3: The student completes, with at least a grade of B, the special seminar course 15.099 OR in the Real World in which students build and implement models that address real-world OR applications or course 15.071 The Analytics Edge. Note: students taking these courses may choose to NOT have it fulfill their Hands-on requirement if they would prefer to satisfy it via option 1 or 2.

Computing – In addition, each student must have a working knowledge of a computer language.

Examples include: C or C++, Pearl or Python. To satisfy this requirement, a student may either take course 1.001; or attempt to waive the requirement by submitting a petition to the ORC co-directors outlining his/ her knowledge and experience with a computer language.

PhD students must also submit an approved thesis based on their independent research. The Institute sets no specific minimum number of credits for the award of the doctoral degree.

These and additional subjects in operations research and closely related fields are listed under Subject Offerings; that section also includes some of the electives that a student may select in structuring a program.

Qualifying Examinations

Doctoral degree candidates must pass the Qualifying Examination, taken at the beginning of their second year of study at MIT. The Qualifying Examination is based on material covered in the subjects:

  • 6.251J/15.081J Introduction to Mathematical Programming
  • 6.436J/15.085J Fundamentals of Probability

The Qualifying Examination consists of two written parts, offered one week apart. The exam is usually administered before Registration Day.

General Examination

Once PhD students have passed the Qualifying Examination and have completed the necessary program requirements, they must pass the ORC General Examination (usually taken at the end of the second year of graduate study). The exam is composed of a research-oriented (RO) paper, and an extensive oral examination.

MIT home