A manufacturing-environmental model using bayesian belief networks for assembly design decision support

Wooi Ping Cheah; Kyoung Yun Kim; Hyung Jeong Yang; Sook Young Choi; Hyung Jae Lee

doi:10.1007/978-3-540-73325-6_37

A manufacturing-environmental model using bayesian belief networks for assembly design decision support

Wooi Ping Cheah, Kyoung Yun Kim, Hyung Jeong Yang, Sook Young Choi, Hyung Jae Lee

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN.

Original language	English
Title of host publication	New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings
Publisher	Springer Verlag
Pages	374-383
Number of pages	10
ISBN (Print)	9783540733225
DOIs	https://doi.org/10.1007/978-3-540-73325-6_37
Publication status	Published - 2007
Externally published	Yes
Event	20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007 - Kyoto, Japan Duration: 26 Jun 2007 → 29 Jun 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4570 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007
Country/Territory	Japan
City	Kyoto
Period	26/06/07 → 29/06/07

Keywords

Applications to manufacturing
Assembly design
Bayesian belief networks
Decision support
Fuzzy cognitive maps

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-73325-6_37

Cite this

Cheah, W. P., Kim, K. Y., Yang, H. J., Choi, S. Y., & Lee, H. J. (2007). A manufacturing-environmental model using bayesian belief networks for assembly design decision support. In New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings (pp. 374-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4570 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-73325-6_37

Cheah, Wooi Ping ; Kim, Kyoung Yun ; Yang, Hyung Jeong et al. / A manufacturing-environmental model using bayesian belief networks for assembly design decision support. New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings. Springer Verlag, 2007. pp. 374-383 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN.",

keywords = "Applications to manufacturing, Assembly design, Bayesian belief networks, Decision support, Fuzzy cognitive maps",

author = "Cheah, {Wooi Ping} and Kim, {Kyoung Yun} and Yang, {Hyung Jeong} and Choi, {Sook Young} and Lee, {Hyung Jae}",

year = "2007",

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Cheah, WP, Kim, KY, Yang, HJ, Choi, SY & Lee, HJ 2007, A manufacturing-environmental model using bayesian belief networks for assembly design decision support. in New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4570 LNAI, Springer Verlag, pp. 374-383, 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007, Kyoto, Japan, 26/06/07. https://doi.org/10.1007/978-3-540-73325-6_37

A manufacturing-environmental model using bayesian belief networks for assembly design decision support. / Cheah, Wooi Ping; Kim, Kyoung Yun; Yang, Hyung Jeong et al.
New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings. Springer Verlag, 2007. p. 374-383 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4570 LNAI).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A manufacturing-environmental model using bayesian belief networks for assembly design decision support

AU - Cheah, Wooi Ping

AU - Kim, Kyoung Yun

AU - Yang, Hyung Jeong

AU - Choi, Sook Young

AU - Lee, Hyung Jae

PY - 2007

Y1 - 2007

N2 - Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN.

AB - Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN.

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KW - Assembly design

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KW - Decision support

KW - Fuzzy cognitive maps

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings

PB - Springer Verlag

T2 - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007

Y2 - 26 June 2007 through 29 June 2007

ER -

Cheah WP, Kim KY, Yang HJ, Choi SY, Lee HJ. A manufacturing-environmental model using bayesian belief networks for assembly design decision support. In New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings. Springer Verlag. 2007. p. 374-383. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-73325-6_37

A manufacturing-environmental model using bayesian belief networks for assembly design decision support

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Keywords

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