Making design decisions under uncertainties: probabilistic reasoning and robust product design

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Paul Christoph Gembarski
  • Stefan Plappert
  • Roland Lachmayer
View graph of relations

Details

Original languageEnglish
Pages (from-to)563-581
Number of pages19
JournalJournal of Intelligent Information Systems
Volume57
Issue number3
Early online date19 Aug 2021
Publication statusPublished - 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 artifacts 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

Cite this

Making design decisions under uncertainties: probabilistic reasoning and robust product design. / Gembarski, Paul Christoph; Plappert, Stefan; Lachmayer, Roland.
In: Journal of Intelligent Information Systems, Vol. 57, No. 3, 12.2021, p. 563-581.

Research output: Contribution to journalArticleResearchpeer review

Gembarski PC, Plappert S, Lachmayer R. Making design decisions under uncertainties: probabilistic reasoning and robust product design. Journal of Intelligent Information Systems. 2021 Dec;57(3):563-581. Epub 2021 Aug 19. doi: 10.1007/s10844-021-00665-6
Gembarski, Paul Christoph ; Plappert, Stefan ; Lachmayer, Roland. / Making design decisions under uncertainties : probabilistic reasoning and robust product design. In: Journal of Intelligent Information Systems. 2021 ; Vol. 57, No. 3. pp. 563-581.
Download
@article{a0697d68c96d4195b2affe17e1931d1d,
title = "Making design decisions under uncertainties: probabilistic reasoning and robust product design",
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",
author = "Gembarski, {Paul Christoph} and Stefan Plappert and Roland Lachmayer",
year = "2021",
month = dec,
doi = "10.1007/s10844-021-00665-6",
language = "English",
volume = "57",
pages = "563--581",
journal = "Journal of Intelligent Information Systems",
issn = "0925-9902",
publisher = "Springer Netherlands",
number = "3",

}

Download

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 -