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Modeling and solving technical product configuration problems

Published online by Cambridge University Press:  20 April 2011

Andreas Falkner ,
Alois Haselböck ,
Gottfried Schenner  and
Herwig Schreiner
Andreas Falkner
Affiliation:
Siemens AG Österreich, Corporate Technology Central and Eastern Europe, Research and Technologies, Wien, Austria
Alois Haselböck
Affiliation:
Siemens AG Österreich, Corporate Technology Central and Eastern Europe, Research and Technologies, Wien, Austria
Gottfried Schenner
Affiliation:
Siemens AG Österreich, Corporate Technology Central and Eastern Europe, Research and Technologies, Wien, Austria
Herwig Schreiner
Affiliation:
Siemens AG Österreich, Corporate Technology Central and Eastern Europe, Research and Technologies, Wien, Austria
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Abstract

This paper describes and evaluates approaches to model and solve technical product configuration problems using different artificial intelligence methodologies. By means of a typical example, the benefits and limitations of different artificial intelligence methods are discussed and a flexible software architecture for integrating different solvers in a product configurator is proposed.

Keywords

Artificial IntelligenceConstraint Logic ProgrammingConstraint SatisfactionProduct ConfigurationSAT
Type
Special Issue Articles
Information
AI EDAM , Volume 25 , Issue 2: Configuration , May 2011 , pp. 115 - 129
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Albert, P., Henocque, L., & Kleiner, M. (2008). Ant colony optimization for configuration. Proc. 20th IEEE Int. Conf. Tools With Artificial Intelligence, pp. 247–254.CrossRefGoogle Scholar
Baader, F., McGuinness, D.L., Nardi, D., & Patel-Schneider, P.F. (2003). The Description Logic Handbook. Cambridge: Cambridge University Press.Google Scholar
Cooper, M., Jeavons, P., & Salamon, A. (2008). Hybrid tractable CSP's which generalize tree structure. Proc. ECAI, pp. 530–534.Google Scholar
Dechter, R., & Meiri, I. (1989). Experimental evaluation of preprocessing techniques in constraint satisfaction problems. Proc. 11th IJCAI, pp. 271–277.Google Scholar
Eiter, T., Gottlob, G., & Mannila, H. (1997). Disjunctive datalog. ACM Transactions on Database Systems 22/3, 315–363.Google Scholar
Falkner, A., Feinerer, I., Salzer, G., & Schenner, G. (in press). Computing product configurations via UML and integer linear programming. International Journal on Mass Customization.Google Scholar
Falkner, A., & Haselböck, A. (2009). A simple evaluation process for configurability. Proc. IJCAI-09 Workshop on Configuration, pp. 17–22.Google Scholar
Felfernig, A. (2007). Standardized configuration knowledge representations as technological foundation for mass customization. IEEE Transactions on Engineering Management 54(1), 41–56.Google Scholar
Felfernig, A., Friedrich, G., Jannach, D., Stumptner, M., & Zanker, M. (2003). Configuration knowledge representations for Semantic Web applications. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(1), 31–50.Google Scholar
Felfernig, A., Friedrich, G., Jannach, D., & Zanker, M. (2002). Configuration knowledge representation using UML/OCL. Proc. 5th Int. Conf. Unified Modeling Language, pp. 49–62. Berlin: Springer–Verlag.Google Scholar
Fleischanderl, G., Friedrich, G., Haselböck, A., Schreiner, H., & Stumptner, M. (1998). Configuring large-scale systems with generative constraint satisfaction. IEEE Intelligent Systems 13(4), 59–68.CrossRefGoogle Scholar
Frühwirth, T. (2008). Welcome to constraint handling rules. In Constraint Handling Rules—Current Research Topics (Schrijvers, T., & Frühwirth, T., Eds.), L Vol. 5388. New York: Springer–Verlag.Google Scholar
Gent, I.P., Petrie, K.E., & Puget, J. (2006). Symmetry in constraint programming. In Handbook of Constraint Programming (Rossi, F., van Beek, P., & Walsh, T., Eds.), pp. 329–376. Amsterdam: Elsevier.Google Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization & Machine Learning. Reading, MA: Addison–Wesley Professional.Google Scholar
Gottlob, G., Greco, G., & Mancini, T. (2007). Conditional constraint satisfaction: logical foundations and complexity. Proc. IJCAI 2007, pp. 88–93.Google Scholar
Jackson, D. (2002). Alloy: a lightweight object modeling notation. ACM Transactions on Software Engineering Methodologies 11 2, 256–290.Google Scholar
Khichane, M., Albert, P., & Solnon, C. (2008). Integration of ACO in a constraint programming language. Proc. ANTS, pp. 84–95.Google Scholar
Kirkpatrick, S., Gelatt, C.D. Jr., & Vecchi, M.P. (1983). Optimization by simulated annealing. Science 220(4598), 671–680.Google Scholar
Mayer, W., Bettex, M., Stumptner, M., Falkner, A., & Faltings, B. (2009). On solving complex rack configuration problems using CSP methods. Proc. IJCAI-09 Workshop on Configuration, pp. 53–60.Google Scholar
McDermott, J. (1982). R1: a rule-based configurer of computer systems. Artificial Intelligence 19, 39–88.Google Scholar
Michalewicz, Z., & Fogel, D.B. (2004). How to Solve It: Modern Heuristics. Berlin: Springer.CrossRefGoogle Scholar
Sabin, D., & Weigel, R. (1998). Product configuration frameworks—a survey. IEEE Intelligent Systems 13(4), 42–49.CrossRefGoogle Scholar
Schoofs, L., & Naudts, B. (2000). Solving CSPs with ant colonies. Proc. ANTS, 2000.Google Scholar
Torlak, E. (2009). A constraint solver for software engineering: finding models and cores of large relational specifications. PhD Thesis. MIT.Google Scholar