Normative Decision Analysis in Engineering Design

2006 ◽  
pp. 21-33 ◽  
Author(s):  
Vijitashwa Pandey

The applicability of theoretical decision analysis, while rationally sound, has eluded mainstream engineering design. A reason commonly overlooked is that basic concepts in decision analysis do not scale naturally to multiple attributes — which are encountered in, by far, most design problems. In this paper, we document a paradox when dealing with transactions involving multiple attributes. We show the possibility of a money pump where if we dissociate part of an attribute from a design, the rest of the design can be manipulated to get either a better design or create wealth out of nothing. To reconcile with paradox, it is argued that there is a fundamental problem dealing with multiple attributes where a frame of reference chosen (purposefully) ignores external inputs, assuming that design decisions happen in the vacuum of the frame chosen. For example, in a simple design valuation decision, the money amount committed does not necessarily come from a fixed range of negotiability (upper and lower limits) but is subject to change if significant changes in other attributes are possible. The root cause of this issue is that fungible attributes such as money can form a part of the attribute set or be trivially dissociated from it, if needed. We argue that this is rational behavior on a decision maker’s part. However, most utility formulations do not model it and lead to the paradox. We call this the attribute dissociation problem. A specific definition is provided as well as implications on design as well as preference elicitation methods are considered. Finally, formulations are presented that avoid this problem and recommendations are provided.


Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos ◽  
Matthew P. Castanier

The implications of decision analysis (DA) on engineering design are well known. Recently, the authors proposed decision topologies (DT) as a visual method for making design decisions and proved that they are consistent with normative decision analysis. This paper addresses the practical issue of assessing DTs for a decision maker (DM) using their responses, particularly under uncertainty. This is critical to encoding decision maker preferences so that further analysis and mathematical optimization can be performed using the correct set of preferences. We show how multiattribute DTs can be directly assessed from DM responses. Four methods are shown to evolutionarily assess DTs among which one that requires the DM to rank alternatives and another where a utility function is first assessed. It is also shown that preferences under uncertainty can be easily incorporated. In addition, we show that topologies can be constructed using single attribute topologies similarly to multi-linear functions in utility analysis. This incremental construction simplifies the process of topology construction. The reverse problem of inferring single attribute DTs is also presented. The proposed assessment methods are used on a design decision making problem of a welded beam.


Author(s):  
Michael T. Postek

The term ultimate resolution or resolving power is the very best performance that can be obtained from a scanning electron microscope (SEM) given the optimum instrumental conditions and sample. However, as it relates to SEM users, the conventional definitions of this figure are ambiguous. The numbers quoted for the resolution of an instrument are not only theoretically derived, but are also verified through the direct measurement of images on micrographs. However, the samples commonly used for this purpose are specifically optimized for the measurement of instrument resolution and are most often not typical of the sample used in practical applications.SEM RESOLUTION. Some instruments resolve better than others either due to engineering design or other reasons. There is no definitively accurate definition of how to quantify instrument resolution and its measurement in the SEM.


2007 ◽  
Vol 177 (4S) ◽  
pp. 29-30
Author(s):  
Richard Lee ◽  
Mark A. Callahan ◽  
Glen Schattman ◽  
Philip S. Li ◽  
Marc Goldstein ◽  
...  

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