Decision Making Strategies for Probabilistic Aerospace Systems Design

Decision making;Probabilistic design;Systems design

Borer, Nicholas Keith ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Decision making;    Probabilistic design;    Systems design;   

Others  :  https://smartech.gatech.edu/bitstream/1853/10469/1/borer_nicholas_k_200605_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Modern aerospace systems design problems are often characterized by the necessity to identify and enable multiple tradeoffs.This can be accomplished by transformation of the design problem to a multiple objective optimization formulation.However, existing multiple criteria techniques can lead to unattractive solutions due to their basic assumptions; namely that of monotonically increasing utility and independent decision criteria.Further, it can be difficult to quantify the relative importance of each decision metric, and it is very difficult to view the pertinent tradeoffs for large-scale problems.This thesis presents a discussion and application of Multiple Criteria Decision Making (MCDM) to aerospace systems design and quantifies the complications associated with switching from single to multiple objectives.It then presents a procedure to tackle these problems by utilizing a two-part relative importance model for each criterion. This model contains a static and dynamic portion with respect to the current value of the decision metric.The static portion is selected based on an entropy analogy of each metric within the decision space to alleviate the problems associated with quantifying basic (monotonic) relative importance.This static value is further modified by examination of the interdependence of the decision metrics.The dynamic contribution uses a penalty function approach for any constraints and further reduces the importance of any metric approaching a user-specified threshold level.This reduces the impact of the assumption of monotonically increasing utility by constantly updating the relative importance of a given metric based on its current value.A method is also developed to determine a linearly independent subset of the original requirements, resulting in compact visualization techniques for large-scale problems.