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Preference-directed design

Published online by Cambridge University Press:  27 February 2009

Joseph G. D’Ambrosio  and
William P. Birmingham
Joseph G. D’Ambrosio
Affiliation:
Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48109
William P. Birmingham
Affiliation:
Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48109
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Abstract

Current design practices mandate that engineering designs be evaluated based on multiple attributes, e.g., cost, power, and area. For multiattribute design problems, generation and evaluation of the Pareto optimal set guarantees the optimal design will be found, but is not practical for a large class of problems. Iterative techniques can be applied to most problems, but sacrifice optimality. In this paper, we introduce a new technique that extends the set of design problems that can be solved optimally. By first constructing an imprecise value function, the number of nondominated alternatives that must be generated is reduced. We describe an implementation based on combinatorial optimization and constraint satisfaction which achieves additional performance gains by decomposing the value function to identify dominated design-variable assignments. Test results indicate that our approach extends the set of problems that can be solved optimally.

Keywords

DesignOptimizationUtility TheoryConstraint Satisfaction
Type
Articles
Information
AI EDAM , Volume 9 , Issue 3 , June 1995 , pp. 219 - 230
Copyright
Copyright © Cambridge University Press 1995

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References

REFERENCES

Birmingham, W.P., Gupta, A.P., & Siewiorek, D.P. (1992). Automating the Design of Computer Systems. Jones and Bartlett, Boston.Google Scholar
Bradley, S.R., & Agogino, A.M. (1993). Computer-assisted catalog selection with multiple objectives. ASME Design Theory and Methodology 53, 139–147.Google Scholar
Darr, T., & Birmingham, W.P. (1994). Automated design for concurrent engineering. IEEE Expert, Oct., 35–42.Google Scholar
Dechter, R., & Pearl, J. (1987). Network-based heuristics for constraint-satisfaction problems. Artificial Intelligence 34(1), 1–38.Google Scholar
Fishburn, P.C. (1970). Utility Theory for Decision Making. John Wiley & Sons, New York.Google Scholar
Gebotys, C.H., & Elmasry, M.I. (1993). Global optimization approach for architectural synthesis. Computer Aided Design of Integrated Circuits and Systems 12, Sept., 1266–1278.CrossRefGoogle Scholar
Grossmann, I.E. (1990). Mixed-integer nonlinear programming techniques for synthesis of engineering systems. Research in Engineering Design 1, 205–228.Google Scholar
Hafer, L., & Parker, A. (1983). A formal method for the specification, analysis and design of register-transfer-level digital logic. IEEE Transactions on Computer-Aided Design CAD-2, Jan., 4–17.Google Scholar
Haworth, M.S., & Birmingham, W.P. (1993). Towards optimal system-level design. Proc. of the 30th Design Automation Conference, 434–438.Google Scholar
Keeney, R.L., & Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley and Sons, New York.Google Scholar
King, B., & Akao, Y. (1989). Better Designs in Half the Time-Implementing QFD Quality Function Deployment in America. GOAL/QPC, Metheun, MA.Google Scholar
Lee, J., Hsu, Y., & Lin, Y. (1989). A new integer linear programming formulation for the scheduling problem in data path synthesis. Proc. of the International Conference on Computer Aided Design, 20–23.Google Scholar
Mackworth, A.K. (1987). Constraint satisfaction. In Encyclopedia of Artificial Intelligence, (Shapiro, S.C. (Ed.)), John Wiley & Sons, New York.Google Scholar
Mittal, S., & Falkenhainer, B. (1990). Dynamic constraint satisfaction problems. Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), 25–32.Google Scholar
Mittal, S., & Frayman, F. (1987). A constraint-based expert system for configuration tasks. Proc. of the 2nd International Conference on applications of AI to Engineering.Google Scholar
Murty, K.G. (1983). Linear Programming. John Wiley & Sons, New York.Google Scholar
Nemhauser, G.L., Kan, A.H.G.R., & Todd, M.J. (Eds.). (1989). Optimization. North-Holland, New York.Google Scholar
Peihua, G., & Andrew, A. (1993). Concurrent Engineering-Methodology & Applications. Elsevier, New York.Google Scholar
Pekny, J.G. (1992). Combinatorial optimization in engineering systems: Exploiting problem structure and parallelism. Proc. of the NSF Design and Manufacturing Systems Conference, xx–xx.Google Scholar
Pugh, S. (1990). Total Design. Addison-Wesley, New York.Google Scholar
Sykes, E.A., & White, C.C. (1991). Multiobjective intelligent computer-aided design. IEEE Transactions on Systems, Man, and Cybernetics 21(6), 1498–1511.Google Scholar
Thurston, D.L. (1991). A formal method for subjective design evaluation with multiple attributes. Research in Engineering Design 3, 105–122.Google Scholar
Thurston, D.L. & Carnahan, J.V. (1992). Fuzzy ratings and utility analysis in preliminary design evaluation of multiple attributes. Journal of Mechanical Design 114, Dec., 648–658.Google Scholar
Vanderplaats, G.N. & Sugimoto, H. (1985). Numerical optimization techniques for mechanical design. In Design and Synthesis, Elsevier Science Publishers B.V. (North-Holland), 517–522.Google Scholar
White, C.C. et al. , (1984). A model of multi-attribute decision making and trade-off weight determination under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics SMC-14(2), March/April, 223–229.Google Scholar
Wood, K.L., & Antonsson, E.K. (1989). Computations with imprecise parameters in engineering design: Background and theory. Journal of Mechanisms, Transmissions and automation in Design 111, Dec., 616–625.Google Scholar
Wood, K.L., Antonsson, E.K., & Beck, J.L.( 1990). Representing imprecision in engineering design: Comparing fuzzy analysis and probability calculus. Research in Engineering Design 1, 187–203.Google Scholar
Wu, J. et al. , (1990). A model-based expert system for digital system design. IEEE Design and Test of Computers, Dec., 24–40.Google Scholar
Yu, P.L. (1989). Multiple criteria decision making: Five basic concepts. In Optimization, (Nemhauser, G.L., Kan, A.H.G.R., & Todd, M.J. (Eds.)), pp. 663–699. Elsevier Science Publishers B.V., New York.Google Scholar