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A constraint-based approach to feasibility assessment in preliminary design

Published online by Cambridge University Press:  09 November 2006

ASHWIN GURNANI ,
SCOTT FERGUSON ,
KEMPER LEWIS  and
JOSEPH DONNDELINGER
ASHWIN GURNANI
Affiliation:
Design of Open Engineering Systems Laboratory, University at Buffalo–SUNY, Buffalo, New York, USA
SCOTT FERGUSON
Affiliation:
Design of Open Engineering Systems Laboratory, University at Buffalo–SUNY, Buffalo, New York, USA
KEMPER LEWIS
Affiliation:
Design of Open Engineering Systems Laboratory, University at Buffalo–SUNY, Buffalo, New York, USA
JOSEPH DONNDELINGER
Affiliation:
Vehicle Development Research Lab, General Motors Research & Development Center, Warren, Michigan, USA
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Abstract

In this paper, we present the development and application of a technical feasibility model used in preliminary design to determine whether a set of desired product specifications obtained from marketing is feasible in the engineering domain. This model is developed by integrating the capabilities of a multiobjective design problem, a multicriteria design optimization tool, a Pareto frontier gap analyzer, metamodeling methods, and use of the Pareto frontier as a constraint for feasibility assessment. Although the tools are independent of the domain, their application is illustrated using two examples: a simple three-objective mathematical problem and a five-objective passenger vehicle design problem. The feasibility of the desired product specifications is determined with respect to the problem's Pareto frontier, which is considered to be the necessary constraint to satisfy.

Keywords

Decision MakingDecision Support SystemMultiobjective OptimizationPareto Frontier
Type
Research Article
Information
AI EDAM , Volume 20 , Issue 4 , November 2006 , pp. 351 - 367
Copyright
© 2006 Cambridge University Press

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References

REFERENCES

Alzubbi, A., Ndiaye, A., Mahdavi, B., Guibault, F., Ozell, B., & Trepanier, J.Y. (2000). On the use of JAVA and RMI in the development of a computer framework for MDO. 8th AIAA Symp. Multidisciplinary Analysis and Optimization, Paper No. AIAA-2000-4903, Long Beach, CA.
Azarm, S., Reynolds, B.J., & Narayanan, S. (1999). Comparison of two multi-objective optimization techniques with and within genetic algorithms. ASME Int. Design Engineering Technical Conf., Paper No. DETC99/DAC-8584.
Balling, R.J. (2000). Pareto sets in decision-based design. Engineering Valuation and Cost Analysis 3, 189–198.
Bennett, J.A., Botkin, M.E., Koromilas, C., Lust, R.V., Neal, M.O., Wang, J.T., & Zwiers, R.I.A. (1995). Multidisciplinary framework for preliminary vehicle analysis and design. Proc. ICASE/NASA Langley Workshop on Multidisciplinary Design Optimization, pp. 3–21, SIAM, Hampton, VA.
Coello, C.A.C. & Aguirre, A.H. (2002). Design of combinational logic circuits through an evolutionary multiobjective optimization approach. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16(1), 39–53.Google Scholar
Das, I. & Dennis, J.E. (1997). A closer look at drawbacks of minimizing weighted sums of objectives for pareto set generation in multicriteria optimization problems. Structural Optimization 14(1), 63–69.Google Scholar
Davis, L. (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.
Deb, K. & Kumar, A. (1995). Real-coded genetic algorithms with simulated binary crossover: studies on multimodal and multiobjective problems. Complex Systems 9, 431–454.
Eddy, J. & Lewis, K. (2001). Effective generation of pareto sets using genetic programming. ASME Int. Design Engineering Technical Conf., Paper No. DETC01/DAC-21094, Pittsburgh, PA.
Eldred, M.S., Swiler, L.P., Gay, D.M., Brown, S.L., Giunta, A.A., Wojtkiewicz, S.F., Hart, W.E., & Watson, J.P. (2004). DAKOTA: A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis, Version 3.2 Reference Manual, Paper No. SAND2001-3515. Albuquerque, NM: Sandia National Laboratories.
Fenyes, P.A., Donndelinger, J.A., & and Bourassa, J.F. (2002). A new system for multidisciplinary design and optimization of vehicle architectures. 9th AIAA Symp. Multidisciplinary Analysis and Optimization, Paper No. AIAA-2002-5509, Atlanta, GA.
Ferguson, S., Gurnani, A., Donndelinger, J., & Lewis, K. (2005). A study of convergence and mapping in preliminary vehicle design. International Journal of Vehicle Systems Modeling and Testing 1(1–3), 192–215.Google Scholar
Fonseca, C.M. & Fleming, P.J. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1), 1–16.Google Scholar
Fonseca, C.M. & Fleming, P.J. (1998). Multi-objective optimization and multiple constraint handling with evolutionary algorithms—part 1: a unified formulation. IEEE Transactions on Systems, Management, and Cybernetics—Part A: Systems and Humans 28(1), 26–37.Google Scholar
Fu, Z. & de Pennington, A. (1993). Constraint based design using an operational approach. Research in Engineering Design 5(3/4), 202–217.Google Scholar
Goel, T., Vaidyanathan, R., Haftka, R.T., Queipo, N.V., Shyy, W., & Tucker, K. (2004). Response surface approximation of Pareto optimal front in multi-objective optimization. 10th AIAA/ISSMO Symp. Multidisciplinary Analysis and Optimization, Paper No. AIAA-2004-4501, Albany, NY.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison–Wesley.
Gurnani, A. & Lewis, K. (2005). Robust multiattribute decision making under risk and uncertainty in engineering design. Engineering Optimization 37(8), 813–830.Google Scholar
Kasprzak, E. & Lewis, K. (2000). An approach to facilitate decision trade-offs in Pareto solution sets. Journal of Engineering Valuation and Cost Analysis 3(1), 173–187.Google Scholar
Kosaka, I., Charpentier, C., & Watson, B. (2000). An interface between SDRC I-DEAS and the genesis structural analysis and optimization code. 8th AIAA Symp. Multidisciplinary Analysis and Optimization, Paper No. AIAA-2000-4933, Long Beach, CA.
Koski, J. (1985). Defectiveness of weighting method in multicriterion optimization of structures. Communications in Applied Numerical Methods 1(6), 333–337.Google Scholar
Koza, J., Keane, M., Streeter, M., Adams, T., & Jones, L. (2004). Invention and creativity in automated design by means of genetic programming. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(3), 245–269.Google Scholar
Kurapati, A., Azarm, S., & Wu, J. (2002). Constraint handling improvements for multi-objective genetic algorithms. Structural and Multidisciplinary Optimization 23(3), 204–213.Google Scholar
Lee, J. & Hajela, P. (1996). Parallel genetic algorithm implementation in multidisciplinary rotor blade design. Journal of Aircraft 33(5), 962–969.Google Scholar
Lee, J., Li, D.J., Liu, X., Soderburg, N., Sudjianto, A., Vora, M., & Wang, S. (2001). An approach to robust design employing computer experiments. ASME Int. Design Engineering Technical Conf., Paper No. DETC01/DAC-21094, Pittsburgh, PA.
Longacre, K., Vance, J.M., & DeVries, R.A. (1996). Computer tool to facilitate cross-attribute optimization. 6th AIAA Symp. Multidisciplinary Analysis and Optimization, Paper No. AIAA-96-4132-CP, Bellevue, WA.
Marinescu, R. & Dechter, R. (2005). Advances in AND/OR branch-and-bound search for constraint optimization. 7th Int. Workshop on Preferences and Soft Constraints of the Eleventh Conf. Principles and Practice of Constraint Programming.
Messac, A. & Sundararaj, J.G. (2000). Physical programming's ability to generate a well-distributed set of pareto points. 41st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf., AIAA 2000-1666.
Messac, A., Sundararaj, J.G., Tappeta, R.V., & Renaud, J.E. (2000). The ability of objective functions to generate points on non-convex Pareto frontiers. AIAA Journal 38, 1084–1091.Google Scholar
Miller, B.L. & Goldberg, D.E. (1996). Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation 4(2), 113–131.Google Scholar
Narayanan, S. & Azarm, S. (1999). A multi-objective interactive sequential hybrid optimization technique for design decision making. Engineering Optimization 32, 485–500.Google Scholar
Pareto, V. (1896). Cours D'Économie Politique, Vols. I and II. Laussane, Switzerland: Rouge.
Pareto, V. (1906/1971). Manuale di Econòmica Polìttica [Manual of Political Economy] (Schwier, A.S., Trans.). Milan, Italy/New York: Società Editrice Libràia/Macmillan.
Port, O. (1998). Making complexity look simple: Ford, DuPont, and Tyco are among the fans of breakthrough design software. Business Week May 18, 166.
Röhl, P.J., Kolonay, R.M., Irani, R.K., Sobolewski, M., Kao, K., & Bailey, M.W. (2000). A federated intelligent product environment. 8th AIAA Symp. Multidisciplinary Analysis and Optimization, Paper No. AIAA-2000-4902, Long Beach, CA.
Schmidt, L., Shi, H., & Kerkar, S. (2005). A constraint satisfaction problem approach linking function and grammar-based design generation to assembly. Journal of Mechanical Design 127(2), 196–205.Google Scholar
See, T.-K., Gurnani, A., & Lewis, K. (2004). Multiattribute decision making using hypothetical equivalents and inequivalents. Journal of Mechanical Design 126(6), 950–958.Google Scholar
Siddique, Z. & Boddu, K.R. (2004). A mass customization information framework for integration of customer in configuration/design of a customized product. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(1), 71–85.Google Scholar
Stender, J. (1993). Parallel genetic algorithm: theory and applications. In Frontiers in Artificial Intelligence and Applications, Vol. 14. Amsterdam: IOS Press.
Townsend, J.C., Samareh, J.A., Weston, R.P., & Zorumski, W.E. (1998). Integration of a CAD System Into an MDO Framework, Technical Report NASA/TM-1998-207672. Langley, CA: NASA Langley Research Center.
Viennet, R., Fontiex, C., & Marc, I. (1996). Multiobjective optimization using a genetic algorithm for determining a Pareto set. Journal of Systems Science 27(2), 255–260.Google Scholar