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Course Criteria
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3.00 Credits
An introduction to time series analysis and forecasting. Topics include exploratory data analysis for time-correlated data, time series modeling, spectral analysis, filtering, and state-space models. Time series analysis in both the time domain and frequency domain will be covered. Concentration will be on data analysis with inclusion of important theory.
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3.00 Credits
Seminar on contemporary topics in discrete-event simulation. Topics are determined by student and faculty interests and may include model and simulation theory, validation, experiment design, output analysis, variance-reduction techniques, simulation optimization, parallel and distributed simulation, intelligent simulation systems, animation and output visualization, and application domains. Term project.
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3.00 Credits
Characterization and analysis of problem solving strategies guided by heuristic information. The course links material from optimization, intelligence systems, and complexity analysis. Formal development of the methods and complete discussion of applications, theoretical properties, and evaluation. Methods discussed include best-first strategies for OR and AND/OR graphs, simulated annealing, genetic algorithms and evolutionary programming, tabu search, and tailored heuristics. Applications of these methods to engineering design, scheduling, signal interpretation, and machine intelligence.
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3.00 Credits
A study of technological systems, where decisions are made under conditions of risk and uncertainty. Part I: Conceptualization: the nature of risk, the perception of risk, the epistemology of risk, and the process of risk assessment and management. Part II: Systems engineering tools for risk analysis: basic concepts in probability and decision analysis, event trees, decision trees, and multiobjective analysis. Part III: Methodologies for risk analysis: hierarchical holographic modeling, uncertainty taxonomy, risk of rare and extreme events, statistics of extremes, partitioned multiobjective risk method, multiobjective decision trees, fault trees, multiobjective impact analysis method, uncertainty sensitivity index method, and filtering, ranking, and management method. Case studies.
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3.00 Credits
Topics include stochastic sequential decision models and their applications; stochastic control theory; dynamic programming; finite horizon, infinite horizon models; discounted, undiscounted, and average cost models; Markov decision processes, including stochastic shortest path problems; problems with imperfect state information; stochastic games; computational aspects and suboptimal control, including neuro-dynamic programming; examples: inventory control, maintenance, portfolio selection, optimal stopping, water resource management, and sensor management.
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3.00 Credits
Analyzes the theories and methodologies for optimization with multiple objectives under certainty and uncertainty; structuring of objectives, selection of criteria, modeling and assessment of preferences (strength of preference, risk attitude, and trade-off judgments); vector optimization theory and methods for generating non-dominated solutions. Methods with prior assessment of preferences, methods with progressive assessment of preferences (iterative-interactive methods), methods allowing imprecision in preference assessments; group decision making; building and validation of decision-aiding systems.
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3.00 Credits
Response surface methods provide process and design improvement through the collection and analysis of data from controlled experimentation. This course investigates the construction of response models for systems with discrete and continuous valued responses. The course will cover design of experiments for optimization and methods for building and using response surfaces from simulation, known as simulation-optimization.
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3.00 Credits
A comprehensive treatment of scheduling theory and practice. The formal machine-scheduling problem: assumptions, performance measures, job and flow shops, constructive algorithms for special cases, disjunctive and integer programming formulations, branch-and-bound and dynamic programming approaches, computational complexity and heuristics. Includes alternative scheduling paradigms and scheduling philosophies and software tools in modern applications.
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3.00 Credits
Presents the Bayesian theory of forecasting and decision making; judgmental and statistical forecasting, deterministic and probabilistic forecasting, post-processors of forecasts; sufficient comparisons of forecasters, verification of forecasts, combining forecasts; optimal and suboptimal decision procedures using forecasts including static decision models, sequential decision models, stopping-control models; economic value of forecasts; communication of forecasts; and the design and evaluation of a total forecast-decision system.
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1.00 Credits
Regular meeting of graduate students and faculty for presentation and discussion of contemporary systems problems and research. Offered for credit each semester. Registration may be repeated as necessary.
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