To obtain a Ph.D. degree, the student must complete 48 credit hours of coursework (see below for transfer of credit from other institutions) and 24 hours of research credit. A typical course counts for 3 credits, and a full load for a student with an assistantship is 9 credits per semester, while a full load for a student without an assistantship is 12 credits. (An overload for a student with an assistantship requires the approval of the Dean of the Graduate School. Less than a full load for a student with an assistantship requires the approval of the Graduate Director, which will be granted only in exceptional circumstances. Foreign students may be required by the university to take courses in English.)

The 48 credit hours of coursework must include the following core courses, 3 credits each:

1. 16:198:521 Linear Programming

2. 16:198:522 Network and Combinatorial Optimization Algorithms

3. 16:711:513 Discrete Optimization

4. 16:198:524 Nonlinear Programming Algorithms
    or 16:711:613 Selected Topics in O.R. Nonlinear Programming

5.* 16:960:654 Stochastic Processes or 16:711:525 Stochastic Models of Operations Research

6.* 16:711:555 Stochastic Programming or 16:711:556 Queueing Theory

7. 16:711:548 Case Studies

(*Choose one .)

Courses 1, 2, and 5 should be taken by all students in the first year. The topics in these are tested in Part I of the Ph.D. Qualifying Examination. Students are assumed to have a solid background in linear algebra, analysis, probability, statistics and computers.

The additional courses for the 48 credit hours can be chosen from the wide variety of courses related to Operations Research which are given at Rutgers. Sample courses of interest besides the ones accepted to meet the requirements are:

16:198:503 Data Structures and Algorithms

16:198:510 Numerical Analysis

16:198:513/514 Design and Analysis of Data Structures and Algorithms I/II

16:198:521 Linear Programming

16:198:522 Network and Combinatorial Optimization Algorithms

16:198:524 Nonlinear Programming Alogrithms

16:198:526 Advanced Numerical Analysis

16:198:528 Parallel Numerical Computing

16:198:535 Pattern Recognition Theory and Application

16:198:538 Complexity of Computation

16:198:541 Database Systems

16:220:500 Mathematical Methods for Microeconomics

16:220:501/502 Microeconomic-Theory I/II

16:220:503 Mathematical Methods for Microeconomics

16:220:507/508 Econometrics I/II

16:220:545 Uncertainty and Imperfect Information

16:220:546 Topics in Game Theory

16:540:510 Deterministic Models in Industrial Engineering

16:540:515 Stochastic Models in Industrial Engineering

16:540:520 Design and Physical Distribution Systems

16:540:530 Forecasting and Time Series Analysis

16:540:555 Simulation of Production Systems

16:540:560 Production Analysis

16:540:565 Facilities Planning and Design

16:540:568 Automation and Computer Integrated Manufacturing

16:540:585 System Reliability Engineering

16:540:655 Performance Analysis of Manufacturing Systems

16:540:660 Inventory Control

16:540:665 Theory of Scheduling

16:642:573/574 Numerical Analysis

16:642:577/578 Selected Mathematical Topics in System Theory

16:642:581 Applied Graph Theory

16:642-582/583 Combinatorics I/II

16:642:585 Mathematical Models of Social & Policy Problems

16:642:586 Theory of Measurement

16:642:587 Selected Topics in Discrete Mathematics

16:642:588 Introduction to Mathematical Techniques in Operations Research

16:642:589 Topics in Mathematical Techniques in Operations Research

16:711:517 Computational Projects in Operations Research

16:711:531 Actuarial Mathematics

16:711:553 Boolean and Pseudo-Boolean Functions

16:711:557 Dynamic Programming and Markov Decision Processes

16:711:631 Financial Mathematics

16:711:601/602 Seminar in Operations Research

16:711:611-614 Selected Topics in Operations Research

16:711:695-699 Independent Study in Operations Research

16:711:701/702 Research

16:960:540/541 Statistical Quality Control I/II

16:960:542 Life Data Analysis

16:960:563 Regression Analysis

16:960:567 Applied Multivariate Analysis

16:960:586/587 Interpretation of Data I/II

16:960:590 Design of Experiments

16:960:591 Advanced Design of Experiments

16:960:593 Theory of Statistics

16:960:652/653 Advanced Theory of Statistics

16:960:654 Stochastic Processes

16:960:663 Regression Theory

16:960:680/681 Advanced Probability Theory I/II

16:960:689 Sequential Methods

26:390:571 Survey of Financial Theory

26:390:662 Investment Analysis and Portfolio Theory

26:711:561 Fundamentals of Optimization

26:711:585 Control Models in Operations Management

26:711:586 Planning Models in Operations Management

26:711:652 Non-Linear Programming

26:711:676 Statistical Aspects of Stochastic Simulation

26:960:580 Stochastic Processes

Independent study courses taken from faculty in RUTCOR or in the participating departments in RUTCOR are also encouraged.

At, or prior to, the beginning of the first semester at Rutgers, the students will be tested on calculus, linear algebra, probability theory and statistics. The results will be used to advise students about courses to take, or the steps to take to correct weaknesses in their preparation for the graduate program.

Students are encouraged to discuss their course of study with any of the faculty members of RUTCOR. Students must have their registration or preregistration cards signed each semester by the Graduate Director of RUTCOR, the Associate Graduate Director, or, in their absence, by an appropriately designated faculty member.