Publications Relevant to DBD from Workshop Participants:
Lewis, K., and Mistree, F., 1997, Modeling the Interactions in Multidisciplinary Design: A Game-Theoretic Approach, AIAA Journal of Aircraft, in press.
The development, implementation, and application
of approaches to modeling the interactions in multidisciplinary
design is illustrated. Given that the design of complex systems
involves decisions being made by multiple disciplinary design
teams using associated decision support tools, the task is to
model the real interactions among the designers and their tools
in order to predict the resulting design. Our approach to this
problem is to abstract the interactions in multidisciplinary design
as a sequence of games among a set of players, which are embodied
by the design teams and their computer-based tools. The developments
are applied to a subsonic passenger aircraft design case study
to illustrate the rich insights and results that can be generated
by exercising different realistic protocols between disciplinary
players in modern design processes.
Lewis, K. and Mistree, F., 1995, On Developing a Taxonomy for Multidisciplinary Design Optimization: A Decision-Based Perspective, First World Congress of Structural and Multidisciplinary Optimization, Goslar, Germany, Olhoff, N., and Rozvany, G.I.N., eds., Pergamon Press, pp. 811-818.
In this paper, we approach MDO from a Decision-Based
Design (DBD) perspective and explore classification schemes for
designing complex systems and processes. Specifically, we focus
on decisions, which are only a small portion of the Decision Support
Problem (DSP) Technique, our implementation of DBD. We map coupled
nonhierarchical and hierarchical representations from the DSP
Technique into the Balling-Sobieski framework, and integrate domain-independent
linguistic terms to complete our taxonomy.
Application of DSPs to the design of complex,
multidisciplinary systems include passenger aircraft, ships, damage
tolerant structural and mechanical systems, and thermal energy
systems. In this paper we show that Balling-Sobieski framework
is consistent with that of the Decision Support Problem Technique
through the use of linguistic entities to describe the
same type of formulations. We show that the underlying linguistics
of the solution approaches are the same and can be coalesced into
a homogeneous framework with which to base the research, application,
and technology MDO upon.
Chen, W., and Yuan, C. A Probabilistic Design Model for Achieving Flexibility in Design, 1997 Design Technical Conference, Design Methodology Conference, paper no. DTM-3882, Sacramento, CA, (Sept. 1997).
In the early stages of product development, the transformation
between design requirements and design solutions often involves
uncertainties when specifying the desired target value for the
performance expressed in design requirements. Additionally, to
provide flexibility for later development, the design solution
obtained is desired to be a range rather than a single solution.
Our primary focus in this paper is on developing a probabilistic-based
design model as a basis for providing the flexibility that allows
designs to be readily adapted to changing conditions. This is
obtained by developing a range of design solutions which meet
a ranged set of design requirements. Meanwhile, designers are
allowed to specify the varying degree of desirability of a ranged
set of design requirements based on their preferences. The Design
Preference Index (DPI) is introduced as a design metric to measure
the goodness of flexible designs. Providing the foundation to
our work are the probabilistic representations of design performance
and design preference, the application of robust design concept,
and the utilization of the compromise Decision Support Problem
(DSP) as a multi-objective decision model. A two-bar structural
design is used as an example to demonstrate our approach.
Hazelrigg, G. A., A Framework For Decision-Based Engineering Design, National Science Foundation, Arlington, VA
A Proposed Taxonomy to mechanical design promlems.
|| Classical Decision Theories
|| Applications of Utility Theory to Engineering Design
|| Uncertainty Analysis
|| Theories on Decision-Based Design
|| Design Taxonomy
|| Human Thinking and Learning Process
|| Publications Relevant to DBD from Workshop Participants