ENGINEERING MANAGEMENT AND SYSTEMS ENGINEERING
Professors E.L. Murphree, Jr., H. Eisner, S. Sarkani, T.A. Mazzuchi, J.P. Deason, M.A. Stankosky, J.R. van Dorp
Associate Professors M.R. Duffey, H. Abeledo, J.A. Barbera, G.L. Shaw, J.J. Ryan (Chair)
Assistant Professors J.R. Santos, R.A. Francis, Z. Szajnfarber
Professorial Lecturers C.H. Voas, J.E. Collins, J.W. Harris, Jr.
See the School of Engineering and Applied Science for the programs of study leading to the Bachelor of Science with a major in systems engineering and Bachelor of Arts with a major in applied science and technology.
| 1001 |
Introduction to Systems Analysis (1) |
Mazzuchi and Staff |
| |
A survey of several aspects of systems analysis, including methodologies such as linear programming, network models, probability, and queuing theory, with applications to resource allocation, decision making, and statistical analysis. Spreadsheet and laboratory exercises and projects. (Fall) |
| 2705 |
Mathematics in Operations Research (3) |
Abeledo and Staff |
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Mathematical foundations of optimization theory; linear algebra, advanced calculus, convexity theory. Geometrical interpretations and use of software. Prerequisite: Math 2233. (Spring) |
| 3701 |
Operations Research Methods (3) |
Abeledo and Staff |
| |
Deterministic and stochastic methods. Optimization algorithms: Simplex method, Branch and Bound, combinatorial algorithms, heuristic methods. Optimization theory: convexity, duality, sensitivity analysis. Stochastic optimization: marginal analysis, Markov chains, Markov decision processes. Prerequisite: ApSc 3115 and EMSE 2705, or permission of instructor. (Spring) |
| 3740 |
Systems Thinking and Policy Modeling I (3) |
Santos and Staff |
| |
Introduction to systems thinking and the system dynamics approach to policy analysis, with applications to business management and public policy. Causal-loop and stock and flow models of business growth, technology adoption, and marketing. Use of role-based games to explain key principles of systems. Use of simulation software to model problems and case studies. (Fall) |
| 3760 |
Discrete Systems Simulation (3) |
van Dorp and Staff |
| |
Simulation of discrete stochastic models. Simulation languages. Random-number/random-variate generation. Statistical design and analysis of experiments, terminating/nonterminating simulations; comparison of system designs. Input distributions, variance reduction, validation of models. Prerequisite: ApSc 3115; CSci 1121, 1041, or 1111; or permission of instructor. (Spring) |
| 3850 |
Quantitative Models in Systems Engineering (3) |
Abeledo and Staff |
| |
Quantitative modeling techniques and their application to decision making in systems engineering. Linear, integer, and nonlinear optimization models. Stochastic models: inventory control, queuing systems, and regression analysis. Elements of Monte Carlo and discrete event system simulation. Prerequisite: ApSc 3115. (Fall) |
| 4191 |
Systems Engineering Senior Project (3) |
Duffey, Mazzuchi, and Staff |
| |
Field experience in systems engineering on a team basis. Each small group confronts an actual problem and formulates a solution using systems engineering methods and models. Oral and written reports. Prerequisite or corequisite: EMSE 4710, 4765, 3760, 4755. (Spring) |
| 4197 |
Special Topics (1-3) |
Staff |
| |
May be repeated for credit provided the topic differs. |
| 4198 |
Research (1 to 3) |
Staff |
| |
Applied research and experimentation projects, as arranged. Prerequisite: junior or senior status. (Fall and spring) |
| 4410 |
Survey of Finance and Engineering Economics (3) |
Duffey and Staff |
| |
Survey of material relevant to financial decision-making for engineering activity. Includes traditional engineering economy topics; fundamentals of accounting; and financial planning, budgeting, and estimating applicable to the management of technical organizations. (Fall, spring, and summer) |
| 4710 |
Applied Optimization Modeling (3) |
Abeledo and Staff |
| |
Analysis of linear, integer, and nonlinear optimization models of decision problems that arise in industry, business, and government. Modeling techniques and applications; use of optimization software to solve models. Prerequisite: EMSE 3850 or permission of instructor. (Fall) |
| 4755 |
Quality Control and Acceptance Sampling (3) |
Mazzuchi, Francis, and Staff |
| |
Statistical approaches to quality assurance. Single and multivariate control charts, acceptance sampling by attributes and variables, process capability and design of experiments. Prerequisite: ApSc 3115 or permission of instructor. (Spring) |
| 4765 |
Data Analysis for Engineers and Scientists (3) |
Mazzuchi, van Dorp |
| |
Design of experiments and data collection. Regression, correlation, and prediction. Multivariate analysis, data pooling, and data compression. Model validation. Prerequisite: ApSc 3115. (Fall and spring) |
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