Abstract
The work presented in this chapter deals with problems of invention where solutions of optimization methods do not meet the objectives of problems to solve. The problems previously defined exploit, for their resolution, a problem extending the model of classical TRIZ in a canonical form called “generalized system of contradictions.” This research draws up a resolution process based on the simulation-optimization-invention loop using both solving methods of optimization and invention. More precisely, it models the extraction of generalized contractions from simulation data as combinatorial optimization problems and offers algorithms that provide all the solutions to these problems. In addition, it provides heuristics to select variables and their relevant values involved in generalized contradictions and/or useful for optimization. The contributions concern theory and practice of the inventive design. The work also explores cross-fertilization between optimization and TRIZ.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Altshuller GS (1985) Algorithm of inventive problem solving. http://www.evolocus.com/textbooks/ariz85c.pdf. Accessed 3 Feb 2016
Bonnardel N (2000) Understanding and supporting creativity in design. Knowl Based Syst 13:505–513
Brohm J-M (2003) Les principes de la dialectique. Editions de la Passion
Cameron G (2015) ARIZ explored: a step-by-step guide to ARIZ the algorithm for solving inventive problems. Createspac, Charleston, SC
Collette Y, Siarry P (2004) Multiobjective optimization: principles and case studies. Springer, New York
Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1:131–156
Doreian P (1999) An intuitive introduction to blockmodeling with examples. Bull Méthodol Soc 61:5–34. doi:10.1177/075910639906100103
Dubois S, Eltzer T, De Guio R (2009a) A dialectical based model coherent with inventive and optimization problems. Comput Ind 60:575–583. doi:10.1016/j.compind.2009.05.020
Dubois S, Rasovska I, De Guio R (2009b) Towards an automatic extraction of generalized system of contradictions out of solutionless design of experiments. In: 3nd IFIP working conference on computer aided innovation (CAI): growth and development of CAI, pp 70–79
Dubois S, De Guio R, Rasovska I (2011) Different ways to identify generalized system of contradictions, a strategic meaning. Proc Eng 9:119–125. doi:10.1016/j.proeng.2011.03.105
Eltzer T, DeGuio R (2007) Constraint based modelling as a mean to link dialectical thinking and corporate data. application to the design of experiments. In: León-Rovira N (ed) Trends in computer aided innovation: second IFIP working conference on computer aided innovation, Michigan, USA, 8–9 October 2007. Springer, Boston, pp 145–155
Gano DL (2001) Effective problem solving: a new way of thinking. In: Annual quality congress proceedings
Guyon I (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422
Jain A, Murty M, Flynn PJ, Rosenfeld A, Bowyer K, Ahuja N, Jain A (1999) Data clustering: a review. ACM Comput Surv 31:264–323
Khomenko N, De Guio R (2007) OTSM network of problems for representing and analysing problem situations with computer support. In: Trends in computer aided innovation. 2nd IFIP working conference on computer aided innovation. Technical Center Brighton, Springer, pp 77–88
Lewis DD, Ringuette M (1994) A comparison of two learning algorithms for text categorization. Proc SDAIR 81–93
Liiv I (2010) Seriation and matrix reordering methods: an historical overview. Stat Anal Data Min. doi:10.1002/sam.10071
Lin L (2016) Optimization methods for inventive design. PhD dissertation, University of Strasbourg
Lin L, Rasovska I, De Guio R, Dubois S (2013) Algorithm for identifying generalized technical contradictions in experiments. J Eur Syst Autom 47(4–8):563–588. doi:10.3166/jesa.47.563-588
Lin L, Dubois S, De Guio R, Rasovska I (2015) An exact algorithm to extract the generalized physical contradiction. Int J Interact Des Manuf 9:185–191
Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. Kluwer Academic Publishers, New York
Rasovska I, Dubois S, De Guio R (2010) Study of different principles for automatic identification of generalized system of contradiction out of design of experiments. In: 8th international conference of modeling and simulation, Hammamet, Tunis
Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517. doi:10.1093/bioinformatics/btm344
Sève L (1998) Nature, science, dialectique: un chantier à rouvrir. In: Sève L (ed) Sciences et dialectiques de la nature. Éditions L, pp 23–247
Sève L, Guespin-Michel J (2005) Émergence, complexité et dialectique: sur les systèmes dynamiques non linéaires. Éditions, Paris
Shlens J (2014) A tutorial on principal component analysis. Int J Remote Sens 51
Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. Mach Learn Work Then Conf 412–420. doi:10.1093/bioinformatics/bth267
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Lin, L., Rasovska, I., De Guio, R., Dubois, S. (2017). Optimization Methods for Inventive Design. In: Cavallucci, D. (eds) TRIZ – The Theory of Inventive Problem Solving. Springer, Cham. https://doi.org/10.1007/978-3-319-56593-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-56593-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56592-7
Online ISBN: 978-3-319-56593-4
eBook Packages: Computer ScienceComputer Science (R0)