Details
| Original language | English |
|---|---|
| Pages (from-to) | 563-581 |
| Number of pages | 19 |
| Journal | Journal of Intelligent Information Systems |
| Volume | 57 |
| Issue number | 3 |
| Early online date | 19 Aug 2021 |
| Publication status | Published - Dec 2021 |
Abstract
Making design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made.
Keywords
- Bayesian network, Decision network, Decision-making under uncertainty, Probabilistic reasoning, Solution space development
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Hardware and Architecture
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Artificial Intelligence
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In: Journal of Intelligent Information Systems, Vol. 57, No. 3, 12.2021, p. 563-581.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Making design decisions under uncertainties
T2 - probabilistic reasoning and robust product design
AU - Gembarski, Paul Christoph
AU - Plappert, Stefan
AU - Lachmayer, Roland
PY - 2021/12
Y1 - 2021/12
N2 - Making design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made.
AB - Making design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made.
KW - Bayesian network
KW - Decision network
KW - Decision-making under uncertainty
KW - Probabilistic reasoning
KW - Solution space development
UR - http://www.scopus.com/inward/record.url?scp=85112853501&partnerID=8YFLogxK
U2 - 10.1007/s10844-021-00665-6
DO - 10.1007/s10844-021-00665-6
M3 - Article
AN - SCOPUS:85112853501
VL - 57
SP - 563
EP - 581
JO - Journal of Intelligent Information Systems
JF - Journal of Intelligent Information Systems
SN - 0925-9902
IS - 3
ER -