OR Courses Offered

OR 335/SYST 335 Discrete Systems Simulation Modeling (3:3:0).
Corequisites: CS 112, STAT 344, SYST 202.

An introduction to the basic concepts of modeling complex discrete systems by computer simulation. Topics include Monte-Carlo methods, discrete-event modeling, a specialized simulation language, and the statistics of input and output analysis. (Fall)

OR 441/MATH 441 Deterministic Operations Research (3:3:0).
Prerequisite: MATH 203 or permission of instructor.

A survey of deterministic methods for solving "real-world" decision problems. The linear programming model and simplex method of solution, duality, and sensitivity analysis; transportation and assignment problems; shortest path and maximal flow problems; and an introduction to integer and nonlinear programming are covered. Emphasis is on modeling and problem solving. (Fall), (Spring), (Summer)

OR 442/MATH 442 Stochastic Operations Research (3:3:0).
Prerequisite: STAT 344, MATH 351, or equivalent.

A survey of probabilistic methods for solving decision problems under uncertainty. Probability review, queuing theory, inventory models, reliability, decision theory and games, and simulation are covered. Emphasis is on modeling and problem solving. (Spring)

OR 481/MATH 446 Numerical Methods in Engineering (3:3:0).
Prerequisites: MATH 213 or 215, and MATH 203 or 322; or equivalent.

Modern numerical methods and software. Emphasis is on problem solving through software and assessing the quality of solutions obtained. Topics include computer arithmetic, linear equations and least squares data fitting, interpolation, nonlinear optimization, and differential equations. The course involves extensive computer use. (Fall), (Spring)

OR 498 Independent Study in Operations Research (13:0:0).
Prerequisite: 60 credits; must be arranged with an instructor and approved by the department chair before registering.

Directed self-study of special topics of current interest in operations research. May be repeated for a maximum of six credits if the topics are substantially different. (Fall), (Spring), (Summer)

OR 499 Special Topics in Operations Research (3:3:0).
Prerequisite: 60 credits and permission of instructor; specific prerequisites vary with nature of topic.

Topics of special interest to undergraduates. May be repeated for a maximum of six credits if the topics are substantially different. (Fall), (Spring), (Summer)

OR 540 Management Science (3:3:0).
Prerequisites: MATH 108 and STAT 250 or DESC 200, or equivalent.

Operations research techniques and their application to managerial decision making. Mathematical programming, Markov processes, queuing theory, inventory models, PERT, CPM, and computer simulation are covered, as well as use of contemporary computer software for problem solving. A case-study approach to problem solving is used. OR/MS majors do not receive credit. (Fall), (Spring)

OR 541 Operations Research: Deterministic Models (3:3:0). 
Prerequisite: MATH 203 or equivalent.

Survey of deterministic methods of solving "real world" decision problems. The linear programming model and simplex method of solution, duality, and sensitivity analysis, transportation and assignment problems; shortest path, minimal spanning tree, and maximal flow problems; and an introduction to integer and nonlinear programming are covered. Emphasis on modeling and problem solving. Students who have taken OR 441/MATH 441 will not receive credit. 

OR 542 Operations Research: Stochastic Models (3:3:0).
Prerequisite: STAT 344 or MATH 351, or equivalent. 

A survey of probabilistic methods for solving decision problems under uncertainty. Probability review, reliability, queuing theory, inventory systems, Markov chain models and Markov decision processes, and discrete-event simulation are covered. Emphasis is on modeling and problem solving. Students who have taken OR 442/MATH 442 do not receive credit. 

OR 635 Discrete System Simulation (3:3:0).
Prerequisite: OR 542 or STAT 354 or 344, or equivalent, and knowledge of a scientific programming language.

Computer simulation as a scientific methodology in operations analysis, with emphasis on model development, implementation, and analysis of results. Discrete-event models, specialized languages, experimental design and output statistics are covered. Extensive computational work is required. 

OR 640 Global Optimization and Computational Intelligence (3:3:0). 
Prerequisite: MATH 203 or equivalent and knowledge of a scientific programming language.

The problem of global optimization in the context of large-scale, non-convex mathematical programs and numerical methods for the solution of such programs are presented. Topics covered include: high-level survey of traditional mathematical programming algorithms; critical comparison of metaheuristics and artificial intelligence (AI) algorithms to traditional mathematical programming algorithms; probabilistic search, multi-start methods, statistical tests of performance and confidence, simulated annealing, genetic algorithms, neural networks, Tabu search, homotopies and tunneling; the traveling salesman problem, the Steiner problem, Stackelberg-Cournot-Nash mathematical games and other classical non-convex optimization problems.

OR 641 Linear Programming (3:3:0).
Prerequisite: OR 541 or permission of instructor.

An in-depth look at the theory and methodology of linear programming: Computational enhancements of the revised simplex method; sparse-matrix techniques, bounded variables and the dual simplex method. Alternative interior point methods are described and the computational complexity of various algorithms is analyzed. (Fall)

OR 642 Integer Programming (3:3:0).
Prerequisite: OR 541 or permission of instructor.

Cutting plane and enumeration algorithms for solution of integer linear programs; bounding strategies and reformulation techniques; heuristic approaches to the solution of complex problems; knapsack problems, matching problems, set covering and partitioning problems; applications to problems in OR/MS, such as capital budgeting, facility location, political redistricting, engineering design, and scheduling. (Spring)

OR 643 Network Modeling (3:3:0).
Prerequisites: OR 541 or permission of instructor.

An introduction to network problems in operations research, computer science, electrical engineering, and systems engineering. Solution techniques for various classes of such problems are developed. Topics include minimal-cost network flow, maximal flow, shortest path, and generalized networks; plus stochastic networks, network reliability, and combinatorially-based network problems. The complexity of each problem class is also analyzed.(Fall)

OR 644 Nonlinear Programming (3:3:0)
Prerequisites: MATH 213 or equivalent and OR 541 or permission of instructor.

Nonlinear optimization theory and techniques applicable to problems in engineering, economics, operations research, and management science. The course covers convex sets and functions, optimality criteria and duality; algorithms for unconstrained minimization, including descent methods, conjugate directions, Newton-type and quasi-Newton methods; and algorithms for constrained optimization, including active set methods and penalty and barrier methods.(Spring)

OR 645/STAT 645 Stochastic Processes (3:3:0).
Prerequisite: OR 542, STAT 544, or permission of instructor.

Selected applied probability models including Poisson processes, discrete- and continuous-time Markov chains, renewal and regenerative processes, semi-Markov processes, queuing and inventory systems, reliability theory, and stochastic networks. Emphasis is on applications in practice as well as analytical models. 

OR 647 Queuing Theory (3:3:0).
Prerequisite: OR 542, STAT 544, or permission of instructor.

A unified approach to queuing organized by type of model. Single- and multiple-channel exponential queues; Erlangian models, bulk and priority queues, networks of queues; general arrival and/or service times; and statistical inference and simulation of queues are covered. Extensive use of computational software. (Spring)

OR 648 Production and Inventory Systems (3:3:0).
Prerequisites: OR 541 and OR 542, or permission of instructor.

An analysis of production and inventory systems. The use of mathematical modeling for solutions of production planning and inventory control problems is introduced. Also included are stochastic inventory systems of lot sized-reorder type; periodic review and single-period models; application of dynamic programming theory to deterministic and stochastic cases; and static and dynamic production-planning models. 

OR 649 Topics in Operations Research (3:3:0).
Prerequisite: Permission of instructor.

An advanced topic chosen according to interests of students and the instructor from dynamic programming, inventory theory, queuing theory, Markov and semi-Markov decision processes, reliability theory, decision theory, network flows, large-scale linear programming, nonlinear programming, and combinatorics. May be repeated for a maximum of six credits if the topics are substantially different. 

OR 651 Military Operations Research I: Cost Analysis (3:3:0).
Corequisites: OR 541or OR 542.

While drawing on other disciplines (e.g., managerial accounting, econometrics, systems analysis, etc.), cost analysis uses operations research to assist decision makers in choosing preferred future courses of action by evaluating selected alternatives on the basis of their costs, benefits, and risks. Cost analysis is distinctly different from cost estimating in that projecting future courses of action almost always requires mathematical modeling. Topics include analysis overview, economic analysis, estimating relationships (factors, simple and complex models), acquiring and verifying cost data, cost progress curves, life cycle costing, scheduling estimating, effectiveness and risk estimation, relationship of effectiveness models and measures to cost analysis. 

OR 652 Military Operations Research Modeling II: Effectiveness Analysis (3:3:0).
Corequisites: OR 541or OR 542. 

Examines the issues and modeling underlying military decisions at the Military Service, Joint Staff, and Department of Defense level. Analytical methods with applications to theater campaign analysis, equipment and weapon system modernization, force structure development, strategic mobility and deployment, small scale contingency operations, logistics, and requirements determination are considered. Optimization, simulation, and statistical techniques are stressed. Realistic problems are presented and solved by the students as case studies. Display of results and presentation techniques for military decision makers are emphasized. 

OR 671/SYST 671 Judgment and Choice Processing and Decision Making (3:3:0).
Prerequisite: STAT 510 or 554 or permission of instructor.

A study of intuitive nature of human judgment and decision making, and some methods currently being used for improving individual and group decisions. The nature of judgment emphasizing limitations on human information processing abilities, and the use of decision- analytic techniques to improve decision making are covered. 

OR 675/STAT 678/SYST 675 Reliability Analysis (3:3:0). 
Prerequisite: STAT 544 or 554 or permission of instructor

An introduction to component and system reliability, their relationship, and problems of inference. Topics include component lifetime distributions and hazard functions, parameter estimation and hypothesis testing, life testing, accelerated life testing, system structural functions, and system maintainability. 

OR 677/STAT 677/SYST 677 Statistical Process Control (3:3:0).
Prerequisite: STAT 544 or 554, or permission of instructor. 

An introduction to the concepts of quality control and reliability. Acceptance sampling, control charts, and economic design of quality control systems are discussed, as are system reliability, fault-tree analysis, life testing, repairable systems, and the role of reliability, quality control and maintainability in life-cycle costing. The role of MIL and ANSI standards in reliability and quality programs is also considered. 

OR 680 Project Course in Operations Research, Systems Engineering and Computational Modeling (3:3:0).
Prerequisites: 21 graduate credits in OR or SYST.

This course is designed to be the capstone course for both the master'Spring program in operations research and the capstone course for the certificate in computational modeling. It can also be used in lieu of an individual research project in the master'Spring program in systems engineering. The focus is on model development and implementation involved in the practice of operational modeling. A key activity is the completion of a major applied group project. Work includes project proposal planning, completion, documentation, and presentation. 

OR 681Contemporary Issues in Decision Analysis (3:3:0).
Prerequisite: OR 542 or MBA 638.

Application of analytic reasoning and skills to practical problems in decision-making. Topics include problem structure, and analysis and solution implementation, emphasizing contemporary approaches to decision analytic techniques.(Fall)

OR 682/CSI 700 Computational Methods in Engineering and Statistics (3:3:0).
Prerequisite: MATH 203 and 213 or equivalent, modern numerical methods and software. 

Numerical methods have been developed to solve mathematical problems that lack explicit closed-form solutions or have solutions that are not amenable to computer calculations. Examples include solving differential equations or computation probabilities. This course discusses numerical methods for such problems as regression, analysis of variance, nonlinear equations, differential and difference equations and nonlinear optimization. Applications in statistics and engineering are emphasized. The course involves extensive computer use. 

OR 683 Principles of Command, Control, Communications, and Intelligence (C3I) (3:3:0).
Prerequisite: ECE 528 or OR 542 or equivalent. 

Fundamental principles of C3I are developed from a descriptive, theoretical, and quantitative perspective. The principles and techniques are applicable to a wide range of civilian and military situations. Topics include C2 process; modeling and simulation for combat operations; detection, sensing, and tracking; data fusion and situation assessment; optimal decision making; methodologies and tools of C3I architectures; tools for modeling and evaluations of C3 systems such as queuing theory are also included.(Fall)

OR 690 Optimization of Supply Chains (3:3:0).
Prerequisites: graduate standing, mathematics through linear algebra and STAT 344. 

This course focuses on both supply chain optimization from an Enterprise-wide perspective, and supply chain optimization from a business-to-business E-commerce concern. Thus the course is concerned with optimizing the value of goods and services and assuring a reasonable return on such sales. The course describes both heuristic and exact algorithms for scheduling, production, inventory management, logistics, and distribution. New software that enables such optimization is presented and manufacturing and service examples from the public and private sectors are presented. New techniques to handle risk, quality of data, and robustness of solutions are presented. Students perform case studies using state-of-the-art software. 

OR 741 Advanced Linear Programming (3:3:0).
Prerequisites: OR 541 and OR 641.

Recent developments in linear programming. The course highlights advances in interior point methods and also addresses developments in the simplex method. Projective methods, affine methods, and path-following methods are examined, including Karmarkar'Spring original work,. The relationships between these methods are discussed, as well as their relationships to methods in nonlinear programming. Also discussed are advances in data structures and other implementation issues. Students have the opportunity to test software and solve large-scale linear programs. 

OR 750 Advanced Topics in Operations Research (3:3:0).
Prerequisites: OR 541 or OR 542 and a 600-level course that will vary with the content of the course.

Special topics, applications, and/or recent developments in operations research. Contents vary and may include topics in optimization, stochastic methods, or decision support that are not covered in the standard OR curriculum. May be repeated for credit when topics are distinctly different. 

OR 751 Advanced Topics in Operations Research for Planning, Scheduling, and Network Design (3:3:0).
Prerequisite: OR 642 or OR 643 or OR 690. 

An introduction to network and combinatorial optimization problems in logistics, computer science, electrical engineering, and systems engineering. Solution techniques for various classes of such problems are developed. Topics include scheduling algorithms, capital budgeting, minimal cost network flow, optimal routings, and generalized networks. Scheduling algorithms, network reliability, stochastic networks and combinatorially-based network problems are discussed.