Online Systems Engineering M.ENG.

This course emphasizes the development of modeling and simulation concepts and analysis skills necessary to design, program, implement, and use computers to solve complex systems/products analysis problems. The key emphasis is on problem formulation, model building, data analysis, solution techniques, and evaluation of alternative designs/ processes in complex systems/products. Overview of modeling techniques and methods used in decision analysis, including Monte Carlo and discrete event simulation is presented.

This project-based course exposes students to tools and methodologies useful for forming and managing an effective engineering design team in a business environment. Topics covered will include: personality profiles for creating teams with balanced diversity; computational tools for project coordination and management; real time electronic documentation as a critical design process variable; and methods for refining project requirements to ensure that the team addresses the right problem with the right solution.

This course presents the fundamental principles and process for designing effective and reliable, supportable, and maintainable systems. The participants will also understand the concept of system operational effectiveness, and the inherent "cause and effect" relationship between design decisions and system operation, maintenance and logistics. Furthermore, the course will also discuss system life cycle cost modeling as a strategic design decision making methodology and present illustrative case studies.

This course discusses the fundamentals of system architecting and the architecting process, along with practical heuristics. Furthermore, the course has a strong "how-to" orientation, and numerous case studies are used to convey and discuss good architectural concepts as well as lessons learned. Adaptation of the architectural process to ensure effective application of COTS will also be discussed. In this regard, the course participants will be introduced to an architectural assessment and evaluation model. Linkages between early architectural decisions, driven by customer requirements and concept of operations, and the system operational and support costs are highlighted.

This course will explore and discuss issues related to the integration and testing of complex systems. First and foremost, students will be exposed to issues relating to the formulation of system operational assessment and concept. Subsequently, functional modeling and analysis methods will be used to represent the system functionality and capability, leading to the packaging of these functions and capabilities into high-level system architecture. Specific focus will be given to issues of interface management and testability. The course will also address the related management issues pertaining to integrated product teams, vendors and suppliers, and subcontractors. In addition, selected articles will be researched to demonstrate the techniques explored in class.

The supportability of a system can be defined as the ability of the system to be supported in a cost effective and timely manner, with a minimum of logistics support resources. The required resources might include test and support equipment, trained maintenance personnel, spare and repair parts, technical documentation and special facilities. For large complex systems, supportability considerations may be significant and often have a major impact upon life-cycle cost. It is therefore particularly important that these considerations be included early during the system design trade studies and design decision-making.

This course is a study of analytic techniques for rational decision-making that addresses uncertainty, conflicting objectives, and risk attitudes. This course covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees and influence diagrams; solving value hierarchies; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analyses.

This course provides the participant with the tools and techniques that can be used early in the design phase to effectively influence a design from the perspective of system reliability, maintainability, and supportability. Students will be introduced to various requirements definition and analysis tools and techniques to include Quality Function Deployment, Input-Output Matrices, and Parameter Taxonomies. An overview of the system functional analysis and system architecture development heuristics will be provided. Further, the students will learn to exploit this phase of the system design and development process to impart enhanced reliability, maintainability, and supportability to the design configuration being developed. Given the strategic nature of early design decisions, the participants will also learn selected multiattribute design decision and risk analysis methodologies, including Analytic Hierarchy Process (AHP). As part of the emphasis on maintainability, the module addresses issues such as accessibility, standardization, modularization, testability, mobility, interchangeability and serviceability, and the relevant methods, tools, and techniques. Further, the students will learn to exploit this phase of the system design and development process to impart enhanced supportability to the design configuration being developed through an explicit focus on configuration commonality and interchangeability, use of standard parts and fasteners, adherence to open system standards and profiles, and use of standard networking and communication protocols. Examples and case studies will be used to facilitate understanding of these principles and concepts.

This course introduces students to the software design process and it’s models; representations of design/architecture; software architectures and design plans; design methods; design state assessment; design quality assurance; and design verification.

This course introduces students to systematic testing of software systems, software verification, symbolic execution, software debugging, quality assurance, measurement and prediction of software reliability, project management, software maintenance, software reuse and reverse engineering.