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Courses

The MOR Degree requires a minimum of 30 credit hours. To earn the Master’s of Operations Research, the student must:

1. Successfully complete five Core courses from the following list. Exceptions to this list may be made on a case-by-case basis.

Units: 3

The course aims to introduce the various types of operations research models and techniques. We will address how to formulate a wide range of decision problems using an appropriate mathematical programming model and solve them using an appropriate algorithm or solver. The emphasis will be given to Linear Programming, Network Models, and Integer Programming. Some example applications of mathematical programming to be covered in this class include production planning, network analysis, project scheduling, logistics network design, fixed charge problems, set covering problem, etc.

Offered in Fall Spring Summer


Units: 3

Basic concepts of linear, nonlinear and dynamic programming theory. Not for majors in OR at Ph.D. level.

Offered in Fall Only


Units: 3

Algorithm design techniques: use of data structures, divide and conquer, dynamic programming, greedy techniques, local and global search. Complexity and analysis of algorithms: asymptotic analysis, worst case and average case, recurrences, lower bounds, NP-completeness. Algorithms for classical problems including sorting, searching and graph problems [connectivity, shortest paths, minimum spanning trees].

Offered in Fall Spring Summer


Units: 3

Introduction to methods for obtaining approximate solutions to unconstrained and constrained minimization problems of moderate size. Emphasis on geometrical interpretation and actual coordinate descent, steepest descent, Newton and quasi-Newton methods, conjugate gradient search, gradient projection and penalty function methods for constrained problems. Specialized problems and algorithms treated as time permits.

Offered in Fall Only


Units: 3

Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom and split-plot experiments. Computing laboratory addressing computational issues and use of statistical software.

Offered in Fall Spring Summer


Units: 3

This course is intended to give students a background in the methods of statistical analysis and design of experiments that will assist them in conducting research and analyzing data in engineering. Concentration in this course will be on principles of the design of experiments and analysis of variance and regression including post-hoc tests, inference for simple regression, multiple regression, and curvilinear regression.

Offered in Fall and Spring


Units: 3

In this course, graduate students will learn basic data science methodologies. Examples of the methodologies include linear regression, generalized linear models, regularization and variable selection, and dimensionality reduction. In addition, students will also learn how to use these methods to solve real-world Industrial Engineering-related problems by analyzing industrial datasets and projects.

Offered in Spring Only


Units: 1 - 6

Special developments in some phase of industrial engineering using traditional course format. Identification of various specific topics and prerequisites for each section from term to term.


Units: 3

ISE/OR 560 will introduce mathematical modeling, analysis, and solution procedures applicable to uncertain [stochastic] production and service systems. Methodologies covered include probability theory and stochastic processes including discrete and continuous Markov processes. Applications relate to design and analysis of problems, capacity planning, inventory control, waiting lines, and service systems.

Offered in Fall Only


Units: 3

This course concentrates on design, construction, and use of discrete/continuous simulation object-based models employing the SIMIO software, with application to manufacturing, service, and healthcare. The focus is on methods for modeling and analyzing complex problems using simulation objects. Analysis includes data-based modeling, process design, input modeling, output analysis, and the use of 3D animation with other graphical displays. Object-oriented modeling is used to extend models and enhance re-usability.

Offered in Spring Only

  • Full core list
  • Restriction 1: At least one of 505, 506, 709 MUST be chosen.
  • Restriction 2: 504 and 505 cannot BOTH be used to satisfy this five course requirement.

2. Electives: Five additional courses should come from mathematics, engineering, statistics, computer science, or other STEM disciplines (e.g., econometrics, data science, etc.). Some business courses are also acceptable as electives. Questions regarding electives should be directed to the OR Director or the student’s faculty advisor.

3. Seminar: Enroll in OR 601 (OR Seminar) for one credit hour during residence. Students are expected to attend the seminar throughout their program.