Journal of Intelligent Manufacturing

, Volume 24, Issue 1, pp 99–111 | Cite as

Applying constraint satisfaction approach to solve product configuration problems with cardinality-based configuration rules

  • Dong Yang
  • Ming Dong


In this paper, the product configuration problems that are characterized by cardinality-based configuration rules are dealt with. Novel configuration rules including FI and EI rules are presented to clarify the semantics of inclusion rules when cardinalities and hierarchies of products are encountered. Then, a configuration graph is proposed to visualize structural rules and configuration rules in product configuration problem. An encoding approach is elaborated to transform the configuration graph as a CSP (Constraint Satisfaction Problem). As a consequence, existing CSP solver, i.e. JCL (Java Constraint Library), is employed to implement the configuration system for product configuration problem with cardinality-related configuration rules. A case study of a bus configuration is used throughout this paper to illustrate the effectiveness of the presented approach.


Product configuration Mass customization Constraint satisfaction 


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  1. Aldanondo M., Hamou K. H., Moynarda G., Lamothe J. (2003) Mass customization and configuration: Requirement analysis and constraint based modeling propositions. Integrated Computer-Aided Engineering 10(2): 177–189Google Scholar
  2. Aldanondo M., Vareilles E. (2008) Configuration for mass customization: How to extend product configuration towards requirements and process configuration. Journal of Intelligent Manufacturing 19: 521–535CrossRefGoogle Scholar
  3. Anselma, L., & Magro, D. (2003). Dynamic problem decomposition in configuration. In Proceedings of configuration workshop, 18th international joint conference on artificial intelligence (IJCAI-03) (pp. 21–26). Mexico: Acapulco.Google Scholar
  4. Barker V. E., O’Connor D. E., Bachant J., Soloway E. (1989) Expert systems for configuration at digital: XCON and beyond. Communications of the ACM 32(3): 298–318CrossRefGoogle Scholar
  5. Barták R., Salido M. A, Rossi F. (2010) Constraint satisfaction techniques in planning and scheduling. Journal of Intelligent Manufacturing 21(1): 5–15CrossRefGoogle Scholar
  6. Czarnecki, K., & Kim, C. H. P. (2005). Cardinality-based feature modeling and constraints: a progress report. In Proceedings of international workshop on software factories. California: San Diego.Google Scholar
  7. Felfernig A., Friedrich G., Jannach D. (2000) UML as domain specific language for the construction of knowledge-based configuration systems. International Journal of Software Engineering and knowledge Engineering 10(4): 449–469CrossRefGoogle Scholar
  8. Felfernig A., Friedrich G., Jannach D. (2002) Conceptual modeling for configuration of mass-customizable products. Artificial Intelligence in Engineering 15(2): 165–176CrossRefGoogle Scholar
  9. Fohn S. M., Liau J. S., Greef A. R., Young R. E., O’Grady P. J. (1995) Configuring computer systems through constraint-based modeling and interactive constraint satisfaction. Computers in Industry 27(1): 3–21CrossRefGoogle Scholar
  10. Hong G., Hu L., Xue D., Tu Y. L., Xiong Y. L. (2008) Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production. International Journal of Production Research 46(12): 3297–3326CrossRefGoogle Scholar
  11. Jiao J., Tseng M. M., Duffy V. G., Lin F. (1998) Product family modeling for mass customization. Computers and Industrial Engineering 35(3-4): 495–498CrossRefGoogle Scholar
  12. Jiao J. X., Simpson T. W., Siddique Z. (2007a) Product family design and platform-based product development: A state-of-the-are review. Journal of Intelligent Manufacturing 18: 5–29CrossRefGoogle Scholar
  13. Jiao J. X., Zhang Y., Wang Y. (2007b) A generic genetic algorithm for product family design. Journal of Intelligent Manufacturing 18(2): 233–247CrossRefGoogle Scholar
  14. Mackworth A. K. (1977) Consistency in networks of relations. Artificial Intelligence 8(1): 99–118CrossRefGoogle Scholar
  15. Marriott K., Stuckey P. J. (1999) Programming with constraints: an introduction. MIT press, CambridgeGoogle Scholar
  16. Mittal, S., & Frayman, F. (1989). Towards a generic model of configuration tasks. In: Proceedings of the 11th international joint conference on artificial intelligence. San Mateo, CA.Google Scholar
  17. Ong S. K., Lin Q., Nee A. Y. C. (2006) Web-based configuration design system for product customization. International Journal of Production Research 44(2): 351–382CrossRefGoogle Scholar
  18. Pine B. J. (1993) Mass customization: The new frontier in business competition. Harvard School Business Press, Boston, MassachusettsGoogle Scholar
  19. Sabin D., Weigel R. (1998) Product configuration frameworks—a survey. IEEE Intelligent System 13(4): 42–49CrossRefGoogle Scholar
  20. Shao X. Y., Wang Z. H., Li P. G., Feng C. X. J. (2006) Integrating data mining and rough set for customer group-based discovery of product configuration rules. International Journal of Production Research 44(14): 2789–2811CrossRefGoogle Scholar
  21. Soininen, T., & Gelle, E. (1999). Dynamic constraint satisfaction in configuration. In: Proceedings of AAAI workshop on configuration(pp. 95–106).Google Scholar
  22. Song Z., Kusiak A. (2009) Optimizing product configurations with a data-mining approach. International Journal of Production Research 47(7): 1733–1751CrossRefGoogle Scholar
  23. Stallman R. M., Sussman G. J. (1977) Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence 9(2): 135–196CrossRefGoogle Scholar
  24. Stumptner M., Friedrich G. E., HaselBock A. (1998) A generative constraint based configuration of large technical systems. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 12(4): 302–320CrossRefGoogle Scholar
  25. Tseng H. E., Chang C. C., Chang S. H. (2005) Applying case-based reasoning for product configuration in mass customization environments. Expert Systems with Applications 29(4): 913–925CrossRefGoogle Scholar
  26. Tsang E. (1993) Foundations of constraint satisfaction. Academic Press, LondonGoogle Scholar
  27. Viappiani, P. (2004). Java Constraints Library (JCL).
  28. Xie H., Henderson P., Kernahan M. (2005) Modelling and solving engineering product configuration problems by constraint satisfaction. International Journal of Production Research 43(20): 4455–4469CrossRefGoogle Scholar
  29. Yeh J. Y., Wu T. S. (2005) Solutions for product configuration management: An empirical study. Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDM) 19(1): 39–47Google Scholar
  30. Yvars P. A. (2009) A CSP approach for the network of product lifecycle constraints consistency in a collaborative design context. Engineering Applications of Artificial Intelligence 22(6): 961–970CrossRefGoogle Scholar
  31. Zhu B., Wang Z., Yang H., Mo R., Zhao Y. (2008) Applying fuzzy multiple attributes decision making for product configuration. Journal of Intelligent Manufacturing 19(5): 591–598CrossRefGoogle Scholar
  32. Zhang J. S., Wang Q. F., Wan L., Zhong Y. F. (2005) Configuration-oriented product modeling and knowledge management for made-to-order manufacturing enterprises. The International Journal of Advanced Manufacturing Technology 25: 41–52CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Information Management, School of Business and ManagementDonghua UniversityShanghaiChina
  2. 2.Department of Operation Management, School of ManagementShanghai Jiao Tong UniversityShanghaiChina

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