This course has been CANCELLED
10–11 September 2017
Decision analysis supports system life cycle development throughout all phases and system hierarchical levels. The course presents the trade study process as part of the systems engineering process, and introduces various decision analysis methods, including the traditional trade study methods, trade space for Cost as Independent Variable (CAIV), Analytic Hierarchy Process (AHV) as a part of the Analytic Network Process (ANP), Potentially All Pairwise Rankings of All Possible Alternatives (PAPRIKA), and Decision Analysis with Uncertain Information/Data.
- Understand the trade study process and its role in the overall systems engineering process.
- Learn the traditional trade study methods: defining selection criteria, identifying weights, identifying alternatives, defining scoring criteria, scoring alternatives, calculating ratings for alternatives, and performing sensitivity analysis.
- Learn how to develop decision trees as hierarchical guidance for different levels of trade studies.
- Learn the trade study role and contribution to Cost as Independent Variable (CAIV).
- Learn how to use and apply decision analysis methods including Analytic Hierarchy Process (AHV) as part of the Analytic Network Process (ANP), Weighted Sum Model (WSM), Potentially All Pairwise Rankings of All Possible Alternatives (PAPRIKA), and Decision Analysis with Uncertain Information/Data.
- Learn how to write a credible, organized, structured, and thorough trade study report.
Who Should Attend
This course will be of interest to engineers, program and project managers, software engineers, information technology specialists, educators, and other professionals who need to learn how to make optimal decisions.
Dr. John C. Hsu has 30 years of diversified experience in systems engineering, aerospace engineering, mechanical engineering, nuclear engineering, software development, and engineering management. He has worked for most of that time at The Boeing Company, as technical manager, project manager, principal investigator, and project leader. Currently, he is the Technical Director of Systems Management and Engineering Consulting Services; Adjunct Professor of California State University at Long Beach, teaching graduate-level systems engineering courses; and Board Member and Lecturer of The University of California Irvine Systems Engineering Certification Program. He implemented the first breakthrough systems engineering applications and is a pioneer in developing and establishing systems engineering processes, methods, templates, and tools for Boeing.
Dr. Hsu is an AIAA Fellow and an Expert Systems Engineering Professional as recognized by the International Council on Systems Engineering. He is a Royal Academy of Engineering Visiting Professor and Honorary Professor of Queens University. He is an Associate Editor of the AIAA Journal of Aircraft and past Chair of the AIAA Systems Engineering Technical Committee. He is currently the Chair of Net-Centric Operations Working Group of INCOSE, focusing on the system-of-systems engineering research. He holds a Ph.D. in mechanical and aerospace engineering, an M.S. in nuclear engineering, and an M.S. in mechanical engineering. He is a registered Professional Engineer.
- Why do we need a trade study?
- Systems engineering process overview
- Role of the trade study and its process
- Traditional trade study methods
- How to determine selection criteria
- Methods (including QFD) for determining weights for selection criteria
- How to identify and down-select too many alternatives
- Introduce different scoring methods for selection criteria
- Score alternatives against selection criteria
- Calculate ratings for alternatives
- Perform sensitivity analysis
- How to write a credible, structured and thorough trade study report
- Develop decision trees
- Study trade space for CAIV
- The Analytic Hierarchy Process (AHP)
- Discuss the process and the difference with Analytic Network Process (ANP)
- Learn the methodology by following an example
- Potentially All Pairwise Rankings of All Possible Alternatives (PAPRIKA)
- Build additive multi-attribute value models with an example
- Learn how to solve too many pairwise combinations and rankings problems
- Decision Analysis with Uncertain information/data
- Maximax method
- Maximin method
- Criterion of realism method
- Equally likely method
- Minimax regret method
- Risk areas/suggestions/guiding principles
Course notes will be made available about one week prior to the course event. You will receive an email with detailed instructions on how to access your course notes. Since these notes will not be distributed on site, AIAA and your course instructor highly recommend that you bring your computer with the course notes already downloaded.
click here to register.
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