Operations Research (OR)
School of Information Technology and Engineering
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. f
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. f,s,su
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. s
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. f,s
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. f,s,sum
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. f,s,sum
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. f,s
542 Operations Research: Stochastic Models (3:3:0). Prerequisite:
STAT 344 or MATH 351, or equivalent. Asurvey 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.
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.f
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. s
643 Network Modeling (3:3:0). Prerequisites: OR 541 and 542 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.f
644 Nonlinear Programming (3:3:0). Prerequisites: MATH 213
or equivalent and knowledge of a scientific programming language.
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,
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.
647 Queuing Theory (3:3:0). Prerequisite: OR 542, STAT544, 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.s
648 Production and Inventory Systems (3:3:0). Prerequisites: OR 541 and 542, or permission of instructor. Ananalysis 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.
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.
651 Military Operations Research I: Cost Analysis (3:3:0).
Corequisites: OR 541 or 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.
671/SYST 671 Judgment and Choice Processing and Decision Making (3:3:0). Prerequisite: STAT 610 or STAT 554 or equivalent. 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.
675/STAT 678/SYST 675 Reliability Analysis (3:3:0). Prerequisite: STAT 554 or equivalent. 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.
677/STAT 677/SYST 677 Statistical Process Control (3:3:0). Prerequisite: STAT 610, STAT 554, or equivalent. 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.
680 Project Course in Operations Research, Systems Engineering and Computational Modeling (3:3:0). This course is designed to be the capstone course for both the master's 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's 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.
681/DESC 744 Contemporary Issues in Decision Analysis (3:3:0). Prerequisite: OR 542 or DESC 611. 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. f
682/CSI 700 Computational Methods in Engineering and Statistics
(3:3:0). Prerequisite: MATH 203 and MATH 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.
741 Advanced Linear Programming (3:3:0). Prerequisites: OR 541 and 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's 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.
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.
751 Advanced Topics in Operations Research for Planning, Scheduling,
and Network Design (3:3:0). Prerequisite: OR 642 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.
George Mason University: 2001-2002 University Catalog: Catalog Index: Course Descriptions:Subject (SUBJ)