Requirements

The requirements for the Ph.D. include:

  • Course work
  • Third semester qualifying examinations
  • First and second year summer 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  3rd 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 8 questions, 5 of which are mandatory while the other 3 are chosen from a list of 6.

Core Courses

Linear Programming

Graph Theory  

Integer Programming

Stochastic Processes (e.g.  Performance Modeling)

Modern Convex Optimization

 

plus 3 from the following list

 

Networks and Matchings

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, Graph Theory, Performance Modeling 

M2:

Networks 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:

Networks OR Advanced LP, Constraint Programming OR Convex Analysis 

M3:

elective, course in minor area

 M4:

 elective, elective