|SUBJECT:||M.S. Thesis Presentation|
|TIME:||Thursday, June 21, 2012, 2:00 p.m.|
|PLACE:||PARB (GT Savannah), 126|
|TITLE:||Model Development Decisions Under Uncertainty in Conceptual Design|
|COMMITTEE:||Dr. Seung-Kyum Choi, Chair (ME)
Dr. Dirk Schaefer (ME)
Dr. Jianxin (Roger) Jiao (ME)
Model development decisions are an important feature of engineering design. The quality of simulation models often dictates the quality of design decisions, seeing as models guide decision makers (DM) in choosing design decisions. A high-quality model accurately represents the modeled system and is helpful for exploring what-if scenarios, optimizing design parameters, estimating design performance, and predicting the effect of design changes. However, obtaining an accurate model necessitates costs in experimentation, labor, model development time, and simulation time. Thus, DMs must make appropriate trade-offs when considering model development decisions. The primary challenge in model development is making decisions under significant uncertainty. This thesis addresses model development in the conceptual design phase where uncertainty levels are high. In the conceptual design phase, there are many information constraints including an incomplete requirements list, unclear design goals, and/or undefined resource constrains. In this thesis, conjoint analysis is employed to solicit the preferences of the decision maker for various model attributes, and the preferences are used to formulate a quasi-objective function during the conceptual design phase—where the overall design goals are vague. The proposed model development framework is used to evaluate the best course of action (i.e., choose a model development decision) for a real-world packaging design problem. The optimization of medical product packaging is assessed via mass spring damper models which predict contact forces experienced during shipping and handling. Novel testing techniques are employed to gather information from drop tests, and preliminary models are developed based on limited information. Ultimately, this thesis presents a model development framework in which decision makers have systematic guidance for choosing optimal model development decisions.