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Constraint-handling techniques for generative product design systems in the mass customization context

Published online by Cambridge University Press:  18 October 2013

Axel Nordin ,
Damien Motte ,
Andreas Hopf ,
Robert Bjärnemo  and
Claus-Christian Eckhardt
Axel Nordin*
Affiliation:
Department of Design Sciences, Faculty of Engineering LTH, Lund University, Lund, Sweden
Damien Motte
Affiliation:
Department of Design Sciences, Faculty of Engineering LTH, Lund University, Lund, Sweden
Andreas Hopf
Affiliation:
Department of Design Sciences, Faculty of Engineering LTH, Lund University, Lund, Sweden
Robert Bjärnemo
Affiliation:
Department of Design Sciences, Faculty of Engineering LTH, Lund University, Lund, Sweden
Claus-Christian Eckhardt
Affiliation:
Department of Design Sciences, Faculty of Engineering LTH, Lund University, Lund, Sweden
*
Reprint requests to: Axel Nordin, Division of Machine Design, Department of Design Sciences, Faculty of Engineering LTH, Lund University, P.O. Box 118, 221 00 Lund, Sweden. E-mail: axel.nordin@mkon.lth.se
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Abstract

Generative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques and the often used weighted sum technique with regard to convergence time, convergence rate, and diversity of the design solutions. The application used for this purpose was a design system aimed at generating a table with an advanced form: a Voronoi diagram based structure. The design problem was constrained in terms of production as well as stability, requiring a time-consuming finite element evaluation. Regarding convergence time and rate, one of the sequential constraint-handling techniques performed significantly better than the weighted sum technique. Nevertheless, the weighted sum technique presented respectable results and therefore remains a relevant technique. Regarding diversity, none of the techniques could generate diverse solutions in a single search run. In contrast, the solutions from different searches were always diverse. Solution diversity is thus gained at the cost of more runs, but no evaluation of the diversity of the solutions is needed. This result is important, because a diversity evaluation function would otherwise have to be developed for every new type of design. Efficient handling of complex constraints is an important step toward mass customization of nontrivial product forms.

Keywords

Complex MorphologiesConstraint-Handling TechniquesEvolutionary ComputingGenerative DesignGenetic Algorithms
Type
Regular Articles
Information
AI EDAM , Volume 27 , Issue 4 , November 2013 , pp. 387 - 399
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Agarwal, M., & Cagan, J. (1998). A blend of different tastes: the language of coffeemakers. Environment and Planning B 25(2), 205–227.CrossRefGoogle Scholar
Agarwal, M., Cagan, J., & Constantine, C.G. (1999). Influencing generative design through continuous evaluation: associating costs with the coffeemaker shape grammar. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13(4), 253–275.CrossRefGoogle Scholar
Ang, M.C., Chau, H.H., McKay, A., & de Pennington, A. (2006). Combining evolutionary algorithms and shape grammars to generate branded product design. Proc. 2nd Design Computing and Cognition Conf., DCC '06, pp. 521–539. Dordrecht: Springer.Google Scholar
Austrell, P.E., Dahlblom, O., Lindemann, J., Olsson, A., Olsson, K.-G., Persson, K., Petersson, H., Ristinmaa, M., Sandberg, G., & Wernberg, P.-A. (2004). CALFEM: A Finite Element Toolbox, Version 3.4. Lund: Lund University, Structural Mechanics LTH.Google Scholar
Beukers, A., & van Hinte, E. (2005). Lightness: The Inevitable Renaissance of Minimum Energy Structures, 4th rev. ed.Rotterdam: 010 Publishers.Google Scholar
Bouché, N. (2009). Keynote V: how could we create new emotional experiences with sensorial stimuli?Proc. 4th Int. Conf. Designing Pleasurable Products and Interfaces, DPPI '09, p. 21. Compiègne, France: Université de Technologie de Compiègne.Google Scholar
Bouyssou, D., Marchant, T., Pirlot, M., Tsoukiàs, A., & Vincke, P. (2006). Evaluation and Decision Models With Multiple Criteria: Stepping Stones for the Analyst. New York: Springer.Google Scholar
Cagan, J. (2001). Engineering shape grammars: where have we been and where are we going? In Formal Engineering Design Synthesis (Antonsson, E.K., & Cagan, J., Eds.), chap. 3, pp. 65–92. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
Chau, H.H., Chen, X., McKay, A., & de Pennington, A. (2004). Evaluation of a 3D shape grammar implementation. Proc.1st Design Computing and Cognition Conf., DCC '04, pp. 357–376. Dordrecht: Kluwer.Google Scholar
Cluzel, F., Yannou, B., & Dihlmann, M. (2012). Using evolutionary design to interactively sketch car silhouettes and stimulate designer's creativity. Engineering Applications of Artificial Intelligence 25(7), 1413–1424.CrossRefGoogle Scholar
Coello Coello, C.A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 191(11–12), 1245–1287.CrossRefGoogle Scholar
Deb, K., & Goldberg, D.E. (1989). An investigation of niche and species formation in genetic function optimization. Proc. 3rd Int. Conf. Genetic Algorithms, ICGA '89, pp. 42–50. Los Altos, CA: Morgan Kaufman.Google Scholar
de Garis, H. (1990). Genetic programming: building artificial nervous systems with genetically programmed neural network modules. Proc. 7th Int. Conf. Machine Learning, pp. 132–139. Los Altos, CA: Morgan Kaufmann.Google Scholar
El-Sayed, A., & Chassapis, C. (2005). A decision-making framework model for design and manufacturing of mechanical transmission system development. Engineering With Computers 21(2), 164–176.Google Scholar
EZCT Architecture & Design Research, Hamda, H., & Schoenauer, M. (2004). Studies on Optimization: Computational Chair Design Using Genetic Algorithms. Paris: EZCT Architecture & Design Research.Google Scholar
Frazer, J.H. (1996). The dynamic evolution of designs. Proc. 4D Dynamics Conf. Design and Research Methodologies for Dynamic Form, pp. 49–53. Leicester: De Montfort University, School of Design & Manufacture.Google Scholar
Friebe, H., & Ramge, T. (2008). Marke Eigenbau: Der Aufstand der Massen gegen die Massenproduktion [The DIY Brand: The Rebellion of the Masses Against Mass Production]. Frankfurt: Campus Verlag.Google Scholar
Goldberg, D.E., & Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. Proc. 2nd Int. Conf. Genetic Algorithms, ICGA '87, pp. 41–49. Hillsdale, NJ: Erlbaum.Google Scholar
Haug, E.J., & Arora, J.S. (1979). Applied Optimal Design—Mechanical and Structural Systems. New York: Wiley.Google Scholar
Hochberg, Y., & Tamhane, A.C. (1987). Multiple Comparison Procedures. New York: Wiley.CrossRefGoogle Scholar
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press.Google Scholar
Howell, D.C. (2007). Statistical Methods for Psychology, 6th ed.Belmont, CA: Thomson Wadsworth.Google Scholar
Jarque, C.M., & Bera, A.K. (1987). A test for normality of observations and regression residuals. International Statistical Review 55(2), 163–172.CrossRefGoogle Scholar
Johansson, J. (2011). How to build flexible design automation systems for manufacturability analysis of the draw bending of aluminum profiles. Journal of Manufacturing Science and Engineering 133(6), 1–11.CrossRefGoogle Scholar
Johnson, L.M. (2012). B-shelves: a web based mass customized product. Master's Thesis. Seattle, WA: University of Washington, Department of Architecture.Google Scholar
Kaplan, A.M., & Haenlein, M. (2006). Toward a parsimonious definition of traditional and electronic mass customization. Journal of Product Innovation Management 23(2), 168–182.CrossRefGoogle Scholar
Knight, T.W. (1980). The generation of Hepplewhite-style chair-back designs. Environment and Planning B 7(2), 227–238.CrossRefGoogle Scholar
Kraft Foods. (2006). Innovate With Kraft. Accessed at http://brands.kraftfoods.com/innovatewithkraft/default.aspx on January 17, 2010.Google Scholar
Kram, R., & Weisshaar, C. (2003). Breeding tables. Accessed at http://www.kramweisshaar.com/projects/breeding-tables.html on February 22, 2012.Google Scholar
Lakhani, K.R., & Kanji, Z. (2009). Threadless: The Business of Community, Harvard Business School Multimedia/Video Case 608-707. Cambridge, MA: Harvard Business School.Google Scholar
Lee, H.C., & Tang, M.X. (2009). Evolving product form designs using parametric shape grammars integrated with genetic programming. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23(2), 131–158.CrossRefGoogle Scholar
Lin, B.-T., Chang, M.-R., Huang, H.-L., & Liu, C.-Y. (2009). Computer-aided structural design of drawing dies for stamping processes based on functional features. International Journal of Advanced Manufacturing Technology 42(11–12), 1140–1152.CrossRefGoogle Scholar
McCormack, J.P., Cagan, J., & Vogel, C.M. (2004). Speaking the Buick language: capturing, understanding, and exploring brand identity with shape grammars. Design Studies 25(1), 1–29.CrossRefGoogle Scholar
Mezura-Montes, E. (2004). Alternative techniques to handle constraints in evolutionary optimization. PhD Thesis. CINVESTAV-IPN, Electrical Engineering Department, Computer Science Section.Google Scholar
Mezura-Montes, E., & Coello Coello, C.A. (2006). A Survey of Constraint-Handling Techniques Based on Evolutionary Multiobjective Optimization, Technical Report EVOCINV-04-2006. CINVESTAV-IPN, Department of Computation, Evolutionary Computation Group.Google Scholar
Michalewicz, Z., Dasgupta, D., Le Riche, R.G., & Schoenauer, M. (1996). Evolutionary algorithms for constrained engineering problems. Computers & Industrial Engineering 30(4), 851–871.CrossRefGoogle Scholar
Michalewicz, Z., & Janikow, C.Z. (1991). Handling constraints in genetic algorithms. Proc. 4th Int. Conf. Genetic Algorithms, ICGA '91, pp. 151–157. San Mateo, CA: Morgan Kaufmann.Google Scholar
Michalewicz, Z., & Schoenauer, M. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4(1), 1–33.CrossRefGoogle Scholar
Moser, K., Müller, M., & Piller, F. (2006). Transforming mass customisation from a marketing instrument to a sustainable business model at Adidas. International Journal of Mass Customisation 1(4), 463–479.CrossRefGoogle Scholar
Motte, D., Nordin, A., & Bjärnemo, R. (2011). Study of the sequential constraint-handling technique for evolutionary optimization with application to structural problems. Proc. 37th Design Automation Conf., DETC/DAC '11, pp. 521–531. Washington, DC: ASME.Google Scholar
Nicewander, W.A., & Price, J.M. (1997). A consonance criterion for choosing sample size. American Statistician 51(4), 311–317.Google Scholar
Nordin, A., Hopf, A., Motte, D., Bjärnemo, R., & Eckhardt, C.-C. (2011). Using genetic algorithms and Voronoi diagrams in product design. Journal of Computing and Information Science in Engineering 11(1), 1–7.Google Scholar
Orsborn, S., Cagan, J., & Boatwright, P. (2008). Automating the creation of shape grammar rules. Proc. 3rd Design Computing and Cognition Conf., DCC '08, pp. 3–22. Dordrecht: Springer Science + Business Media.CrossRefGoogle Scholar
Orsborn, S., Cagan, J., Pawlicki, R., & Smith, R.C. (2006). Creating cross-over vehicles: defining and combining vehicle classes using shape grammars. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20(3), 217–246.CrossRefGoogle Scholar
Paredis, J. (1994). Co-evolutionary constraint satisfaction. Proc. 3rd Parallel Problem Solving from Nature Conf., PPSN III, LNCS, Vol. 866, pp. 46–55. Berlin: Springer.Google Scholar
Pearce, P. (1978). Structure in Nature Is a Strategy for Design. Cambridge, MA: MIT Press.Google Scholar
Petersson, H., Motte, D., Eriksson, M., & Bjärnemo, R. (2012). A computer-based design system for lightweight grippers in the automotive industry. Proc. Int. Mechanical Engineering Congr. Exposition, IMECE '12. Houston, TX: ASME.Google Scholar
Piasecki, M., & Hanna, S. (2010). A redefinition of the paradox of choice. Proc. 4th Design Computing and Cognition Conf., DCC '10, pp. 347–366. Dordrecht: Springer.Google Scholar
Pugliese, M.J., & Cagan, J. (2002). Capturing a rebel: modeling the Harley-Davidson brand through a motorcycle shape grammar. Research in Engineering Design 13(3), 139–156.CrossRefGoogle Scholar
Sandberg, M., & Larsson, T. (2006). Automating redesign of sheet-metal parts in automotive industry using KBE and CBR. Proc. 32nd Design Automation Conf., DETC/DAC '06, pp. 349–357. Philadelphia, PA: ASME.Google Scholar
Schoenauer, M., & Michalewicz, Z. (1996). Evolutionary computation at the edge of feasibility. Proc. 4th Parallel Problem Solving From Nature Conf., PPSN IV, LNCS, Vol. 1141, pp. 245–254. Berlin: Springer.Google Scholar
Schoenauer, M., & Xanthakis, S. (1993). Constrained GA optimization. Proc. 5th Int. Conf. Genetic Algorithms, ICGA '93, pp. 573–580. San Mateo, CA: Morgan Kaufmann.Google Scholar
Shea, K., & Cagan, J. (1999). Languages and semantics of grammatical discrete structures. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13(4), 241–251.CrossRefGoogle Scholar
Toffler, A. (1971). Future Shock, 2nd ed.New York: Bantam Books.Google Scholar
Trubridge, D. (2010). “Coral [lamp].” Accessed at http://www.davidtrubridge.com/coral/ on January 29, 2011.Google Scholar
Van Le, T. (1996). A fuzzy evolutionary approach to constrained optimisation problems. Proc. 3rd IEEE Int. Conf. Evolutionary Computation, ICEC '96, pp. 274–278. Piscataway, NJ: IEEE.Google Scholar
Wenli, Z. (2008). Adaptive interactive evolutionary computation for active intent–oriented design. Proc. 9th Int. Conf. Computer-Aided Industrial Design and Conceptual Design, CAIDCD '08, pp. 274–279. Piscataway, NJ: IEEE.Google Scholar
Wilcox, R.R. (1987). New Statistical Procedures for the Social Sciences. Hillsdale, NJ: Erlbaum.Google Scholar
Yeniay, Ö. (2005). Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications 10(1), 45–56.CrossRefGoogle Scholar