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87
Table 5.5 Sample Data: Utterances of Alternatives
Alternative Carafe Filter
Interval
Glass Steel Plastic Gold tone Paper Titanium
1 13 8 7 0 0 0
2 12 7 6 3 5 2
3 11 6 1 8 15 10
4 9 3 0 5 6 14
While applying the Preference Transition Model and the Utterance-Preference
Model on the utterance data, we can give any initial values to p and q if 0<p<1 and
1/3<q<1 are met. The values will be updated in the later iterations. In this example,
initial values to the parameters of Equations (4.1) & (4.2) are randomly chosen as
p1=0.4 and q1=0.5 for both component selection problems (any initial values to p and q
if 0<p<1 and 1/3<q<1 would be appropriate, as they will be updated in future
iterations). The initial preferential probabilities at the beginning of the design
discussion can be given can be given in several ways: (1) conducting surveys of
designers before the start of the design process, (2) collecting preference information
from an earlier design process, (3) analyzing preferences from the design of similar
products, or (4) establishing an unbiased starting point which assumes a uniform
alternative distribution. This section assigned equal likelihood for initiating PPT as
shown in Time Interval 0 (before the design process). After employing PPT on this
case study, p (from the Preference Transition Model) and q (from the Utterance-
Preference Model) converged to 0.529 and 0.920, respectively, after 19 iterations for
Object Description
| Title | Extraction of preferential probabilities from early stage engineering design team discussion |
| Author | Ji, Haifeng |
| Author email | haifengj@usc.edu; haifeng.ji@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Industrial & Systems Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-08-19 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-07 |
| Advisor (committee chair) | Yang, Maria C. |
| Advisor (committee member) |
Lu, Stephen Jin, Yan |
| Abstract | Activities in the early stage of engineering design typically include the generation of design choices and selection among these design choices. A key notion in design alternative selection is that of preference in which a designer or design team assigns priorities to a set of design choices. However, preferences become more challenging to assign on both a practical and theoretical level when done by a group of individuals. Preferences may also be explicitly obtained via surveys or questionnaires in which designers are asked to rank the choices, rate choice with values, or select a "most-preferred" choice. However, these methods are typically employed at a single point of time; therefore, it may not be practical to use surveys to elicit a team’s preference change and evolution throughout the process.; This research explores the text analysis on the design discussion transcripts and presents a probabilistic approach for implicitly extracting a projection of aggregated preference-related information from the transcripts. The approach in this research graphically represents how likely a choice is to be "most preferred" by a design team over time. For evaluation purpose, two approaches are established for approximating a team's "most preferred" choice in a probabilistic way from surveys of individual team members. A design selection experiment was conducted to determine possible correlations between the preferential probabilities estimated from the team's discussion and survey ratings explicitly stated by team members. Results suggest that there are strong correlations between extracted preferential probabilities and team intents that are stated explicitly, and that the proposed methods can provide a quantitative way to understand and represent qualitative design information using a low overhead information extraction method. |
| Keyword | preferences; probabilities; concept selection; design process; design decision-making |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1635 |
| Contributing entity | University of Southern California |
| Rights | Ji, Haifeng |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Ji-2413 |
| Archival file | uscthesesreloadpub_Volume14/etd-Ji-2413.pdf |
Description
| Title | Page 99 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text |
87
Table 5.5 Sample Data: Utterances of Alternatives
Alternative Carafe Filter
Interval
Glass Steel Plastic Gold tone Paper Titanium
1 13 8 7 0 0 0
2 12 7 6 3 5 2
3 11 6 1 8 15 10
4 9 3 0 5 6 14
While applying the Preference Transition Model and the Utterance-Preference
Model on the utterance data, we can give any initial values to p and q if 0 |
