This course is the first of a two-course sequence that emphasizes modeling and analysis of real-world systems. This course focuses on techniques without consideration of uncertainty or probabilistic effects. The course introduces modeling concepts, scale and measurement principles, mathematical analysis methods, and computational approaches used by systems engineers, operations researchers, and management professionals to support systems design and the Systems Decision Process (SDP). Emphasis is placed on creative application of these topics including system representations, problem formulation, solution methods, analysis of results, and interpretation. Topics include axiomatic design, modularity, value function creation, linear programming, sensitivity analysis, networks, transportation models, and human-systems integration assessment. Cadets will spend several lessons in a computer lab environment.
This course is the first of a two-course sequence that emphasizes modeling and analysis of real-world systems. This course focuses on modeling techniques without consideration of uncertainty or probabilistic effects. The course introduces the deterministic modeling process and many of the classical deterministic models used by systems engineers, operations researchers, and management professionals to identify and analyze alternatives as part of the Systems Decision Process (SDP). Emphasis is placed on creative application of the modeling process to include formulation, solution methods, analysis of results, and interpretation. Topics include deterministic life cycle cost modeling, linear programming, sensitivity analysis, networks, transportation models, dynamic programming and integer programming. Cadets will spend several lessons in a computer lab environment.
This course is the first of a two-course sequence that emphasizes modeling and analysis of real-world systems. This course focuses on modeling techniques without consideration of uncertainty or probabilistic effects. The course introduces the deterministic modeling process and many of the classical deterministic models used by systems engineers, operations researchers, and management professionals to identify and analyze alternatives as part of the Systems Decision Process (SDP). Emphasis is placed on creative application of the modeling process to include formulation, solution methods, analysis of results, and interpretation. Topics include deterministic life cycle cost modeling, linear programming, sensitivity analysis, networks, transportation models, dynamic programming and integer programming. Cadets will spend several lessons in a computer lab environment.
This course is the first of a two-course sequence that emphasizes modeling and analysis of real-world systems. This course focuses on modeling techniques without consideration of uncertainty or probabilistic effects. The course introduces the deterministic modeling process and many of the classical deterministic models used by systems engineers, operations researchers, and management professionals to identify and analyze alternatives as part of the Systems Design Process (SDP). Emphasis is placed on creative application of the modeling process to include formulation, solution methods, analysis of results, and interpretation. Topics include deterministic life cycle cost modeling, linear programming, sensitivity analysis, networks, transportation models, dynamic programming and integer programming. Cadets will spend several lessons in a computer lab environment.
This course is the first of a two-course sequence that emphasizes modeling and analysis of real-world systems. This course addresses systems in which uncertain or probabilistic effects are not important. The course introduces the modeling process and many of the classical deterministic models used by systems engineers, operations researchers, and management professionals to identify and analyze decision alternatives. Emphasis is placed on creative application of the modeling process to include formulation, solution methods, analysis of results, and interpretation. Topics include linear programming, sensitivity analysis, duality, networks, transportation models, dynamic programming and integer programming. Cadets will spend approximately six lessons in a computer lab environment.
This course is the first of a two-course sequence that emphasizes modeling and analysis of real-world systems. This course builds on the scientific method and problem solving paradigms embedded in prerequisite basic science courses. The course introduces the modeling process and many of the classical deterministic models used by systems engineers to analyze decision alternatives. Emphasis is placed on creative application of the modeling process to include formulation, solution methods, analysis of results, and interpretation. Topics include linear programming, sensitivity analysis, duality, networks, transportation models, and advanced topics in deterministic models. Cadets will spend twelve to twenty lessons in a computer lab environment.
The first of a two-course sequence that is part of the engineering science foundation for subsequent courses in systems engineering. This course builds on the scientific method and problem solving paradigms embedded in the basic sciences by introducing cadets to the modeling process and many of the classical deterministic models used by systems engineers to analyze decision alternatives. The emphasis is on the creative application of skills learned in the basic sciences to model formulation, solution methods, analysis of results, and interpretation. Models studied include inventory systems, linear programming and its extensions, networks, and project scheduling (PERT/CPM). The course requires cadets to use computer software for model solution and analysis. Communication skills are developed with both written reports and oral presentations. Cadets will spend twelve to twenty lessons in a computer lab environment.
The first of a two-course sequence that is part of the engineering science foundation for subsequent courses in systems engineering. This course builds on the scientific method and problem solving paradigms embedded in the basic sciences by introducing cadets to the modeling process and many of the classical deterministic models used by systems engineers to analyze decision alternatives. The emphasis is on the creative application of skills learned in the basic sciences to model formulation, solution methods, analysis of results, and interpretation. Models studied include inventory systems, linear programming and its extensions, networks, and project scheduling (PERT/CPM). The course requires cadets to use computer software for model solution and analysis. Communication skills are developed with both written reports and oral presentations.
Lessons
40 @ 55 min (2.500 Att/wk)
Labs
0 @ 0 min
Special requirements
Two group design exercises; compensatory time provided.
