Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T13:41:28.242Z Has data issue: false hasContentIssue false
  • English
  • Français

Taxonomy for classifying engineering decision problems and support systems

Published online by Cambridge University Press:  27 February 2009

David G. Ullman  and
Bruce D'Ambrosio
David G. Ullman
Affiliation:
Department of Mechanical Engineering
Bruce D'Ambrosio
Affiliation:
Department of Computer Science, Oregon State University, Corvaltis, OR 97331-4602, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

The design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams – some are about the product and others about the processes that support the product – some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

Keywords

Decision SupportOptimizationUtility TheoryPERT
Type
Articles
Information
AI EDAM , Volume 9 , Issue 5 , November 1995 , pp. 427 - 438
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Blessing, L. (1994). A Process-Based Approach to Computer-Supported Engineering Design, Thesis, University of Twente, Enschede, the Netherlands.Google Scholar
Computer Select. (1995). CD Rom database of available computer programs.Google Scholar
Cats-Baril, W.L., & Huber, G.P. (1987). Decision support systems for ill-structured problems: An empirical study. Decision Sciences, 18, 350–372.CrossRefGoogle Scholar
Edwards, W., & Barren, F.H. (1995) SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Submitted to Organizational Behavior and Human Decision Processes.CrossRefGoogle Scholar
Haugen, E.B. (1980). Probabilistic mechanical design. Wiley Interscience, New York.Google Scholar
Hopple, G.W. (1988). The state of the art in decision support systems. QED Knowledge Sciences.Google Scholar
Kuipers, B., Moskowitz, A.J., & Kassirer, J.P. (1988). Critical decisions under uncertainty: Representation and structure. Cognitive Science 12, 177–211.CrossRefGoogle Scholar
Nagy, R. (1990). A Knowledge Base Data Representation for Collaborative Mechanical Design. Master Thesis, Department of Mechanical Engineering, Oregon State University.Google Scholar
Nagy, R.L., & Ullman, D.G. (1992). A data representation for collaborative mechanical design. Res. Eng. Design 3 (4), 233–242.CrossRefGoogle Scholar
Pugh, S. (1990). Total design. Addison Wesley.Google Scholar
Rittel, H.W.J., & Webber, M.M. (1973). Dilemmas in a general theory of planning. Policy Sciences, #4, 155–169.CrossRefGoogle Scholar
Siddal, J.N. (1983). Probabilistic engineering design. Marcel Dekker, New York.Google Scholar
Sobieszczanski-Sobieski, J. (1990). Sensitivity of complex, internally coupled systems. AIAA Journal, 28(1).Google Scholar
Stauffer, L. (1987). An empirical study on the process of mechanical design. Doctoral Dissertation, Department of Mechanical Engineering, Oregon State University.Google Scholar
Stauffer, L., Ullman, D.G., & Dietterich, T.G. (1987). Protocol analysis of mechanical engineering design. Proceedings of the 1987 International Conference on Engineering Design, WDK 13, Boston, MA, 68–73.Google Scholar
Steward, D.V. (1981). Systems analysis and management: Structure, strategy and design. Petrocelli Books, New York.Google Scholar
Ullman, D.G., Dietterich, T.G., & Stauffer, L. (1988). A model of the mechanical design process based on empirical data. Artificial Intelligence in Engineering, Design, and Manufacturing. 2(1), 33–52.Google Scholar
Ullman, D.G. (1992). The mechanical design process. McGraw Hill, New York.Google Scholar
Ullman, D.G., & Paasch, R. (1994). Issues critical to the development of design history, design rationale and design intent systems. DTM94, Minneapolis.CrossRefGoogle Scholar
Ullman, D.G., Herling, D., & Stinton, A. (1994). Analysis of protocol data to identify product information evolution and decision making process. Delft Workshop on Protocol Analysis, Delft, the Netherlands.Google Scholar
Yakemovic, K.C.B., & Conklin, J. (1989). The capture of design rationale on an industrial development project: Preliminary report. MCC Technical Report Number STP-279–89.Google Scholar
Yakemovic, K.C.B., & Conklin, J. (1990). Report on a development project use of an issue-based KNOWLEDGE system. MCC Technical Report Number STP-247–90, June 1990.Google Scholar