Simulation-Based Innovization Using Data Mining for Production Systems Analysis

  • Amos H. C. Ng
  • Catarina Dudas
  • Johannes Nießen
  • Kalyanmoy Deb


This chapter introduces a novel methodology for the analysis and optimization of production systems. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. Although the innovization method is based on multi-objective optimization and post-optimality analyses of optimised solutions, it stretches the scope beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. By integrating the concept of innovization with discrete-event simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis, particularly suitable for production systems. The uniqueness of the integrated approach proposed in this chapter lies on applying data mining to the data sets generated from simulation-based multi-objective optimization, in order to automatically or semi-automatically discover and interpret the hidden relationships and patterns for optimal production systems design/reconfiguration. After describing the simulation-based innovization using data mining procedure and its difference from conventional simulation analysis methods, results from an industrial case study carried out for the improvement of an assembly line in an automotive manufacturer will be presented.


Pareto Front Minority Class Pattern Detection Importance Score Industrial Case Study 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Results presented in this case study are based on parts of the research outcomes of the Factory Analyses in ConcepTual phase using Simulation (FACTS) project (2006–2008) and the FFI-HSO (Holistic Simulation Optimization) project (2009–2012). The authors gratefully acknowledge VINNOVA, Sweden, for the provision of research funding for these two projects.


  1. 1.
    Chryssolouris, G. (1992). Manufacturing systems: Theory and practice. New York: Springer.Google Scholar
  2. 2.
    Wu, B. (1992). Manufacturing systems design and analysis (2nd ed.). London: Chapman and Hall.Google Scholar
  3. 3.
    Cochran, D. S., Arinez, J. F., Duda, J. W., & Linck, J. (2002). A decomposition approach for manufacturing system design. Journal of Manufacturing Systems, 20(6), 371–389.CrossRefGoogle Scholar
  4. 4.
    Goldratt, E. M. (1991). Haystack Syndrome. Great Barrington, MA: North River Press.Google Scholar
  5. 5.
    Deb, K., & Srinivasan, A. (2006). Innovization: Innovating design principles through optimization. Proceedings of the Genetic and evolutionary Computation Conference (GECCO-2006), The Association of Computing Machinery (ACM), New York, (pp. 1629–1636).Google Scholar
  6. 6.
    Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (3rd ed.). Wiltshire: Wiley.MATHGoogle Scholar
  7. 7.
    Ng, A. H. C., Urenda, M., & Svensson, J. (2008). Multi-objective simulation optimization for production systems design using FACTS analyzer. Proceedings of the 2 nd Swedish Production Symposium (SPS’08), Stockholm, November 18–20, 2008.Google Scholar
  8. 8.
    Bandaru, S., & Deb, K. (2010). Automating discovery of innovative design principles through optimization. KanGAL Technical Report No.2010001.Google Scholar
  9. 9.
    Dudas, C., Ng, A. H. C., & Boström, H. (2008). Knowledge extraction in manufacturing using data mining. Proceedings of the 2 nd Swedish Production Symposium (SPS’08), Stockholm, November 18–20, 2008.Google Scholar
  10. 10.
    Rokach, L., & Maimon, O. Z. (2008). Data mining with decision trees: Theory and applications. Hackensack, NJ: World Scientific.MATHGoogle Scholar
  11. 11.
    Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39, 27–34.CrossRefGoogle Scholar
  12. 12.
    Han, J., & Kamber, M. (2004). Data mining: Concepts and techniques (7th ed.). San Francisco, Calif: Kaufmann.Google Scholar
  13. 13.
    Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An Overview. AI Magazine, 13, 57–70.Google Scholar
  14. 14.
    Vedder, A. (1999). KDD: The challenge to individualism. Ethics and Information Technology, 1, 275–281.CrossRefGoogle Scholar
  15. 15.
    Sumathi, S., & Sivanandam, S. N. (2006). Introduction to data mining and its applications. Berlin: Springer.MATHCrossRefGoogle Scholar
  16. 16.
    Pachón, V., Jacinto, M., & Maña, M. J. (2009). Practical application of a KDD process to a sulphuric acid plant. In S. Omatu (Ed.), Distributed computing, artificial intelligence, bioinformatics, soft computing, and ambient assisted living: Proceedings of the 10 th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, part II, June 10–12, 2009. Berlin: Springer.Google Scholar
  17. 17.
    Liu, L., & Özsu, M. T. (2009). Encyclopedia of database systems.
  18. 18.
    Weiss, S. M., & Indurkhya, N. (2002). Predictive data mining: A practical guide. San Francisco, CA: Morgan Kaufmann.Google Scholar
  19. 19.
    Neckel, P., & Knobloch, B. (2005). Customer relationship analytics: Praktische anwendung des data mining im CRM (1st ed.). Heidelberg: dpunkt-Verl.Google Scholar
  20. 20.
    Kohavi, R., & Provost, F. (1999). Glossary of terms. Machine Learning 30, 271–274, Scholar
  21. 21.
    Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). New York, NY: Wiley.MATHGoogle Scholar
  22. 22.
    Groth, R. (1998). Data mining: a hands-on approach for business professionals. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
  23. 23.
    Bissantz, N., & Hagedorn, J. (2009). Data mining. Business & Information Systems Engineering, 1, 118–122.CrossRefGoogle Scholar
  24. 24.
    Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38, 1–32.CrossRefGoogle Scholar
  25. 25.
    Nießen, J. (2010). Discovering knowledge from simulation-based evolutionary multi-objective optimization through data mining. MSc dissertation, School of Informatics and Communication, University of Skövde, Sweden.Google Scholar
  26. 26.
    Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, 6(2), 181–197.CrossRefGoogle Scholar
  27. 27.
    Ng, A. H. C., Syberfeldt, A, Grimm, H., & Svensson, J. (2008). Multi-objective simulation optimization and significant dominance for comparing production control mechanisms. Proceedings of the 18 th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM’08), Skövde, Sweden (pp. 1210–1219).Google Scholar
  28. 28.
    Joseph, V. R., & Ying, H. (2008). Orthogonal-maximin Latin hypercube designs. Statistica Sinica, 18, 171–186.MathSciNetMATHGoogle Scholar
  29. 29.
    Liu, A., Ghosh, J., & Martin, C. (2007). Generative oversampling for imbalanced datasets. Proceedings of the 3 rd International Conference in Data Mining (pp. 66–72).Google Scholar
  30. 30.
    Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 341–378.Google Scholar
  31. 31.
    Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1–2), 105–139.CrossRefGoogle Scholar
  32. 32.
    Rule Discovery System, v. 2.6.0, Compumine AB. Retrived April 2010, from
  33. 33.
    Lin, W., Alvarez, S. A., & Ruiz, C. (2002). Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6, 83–105.MathSciNetCrossRefGoogle Scholar
  34. 34.
    Ishibuchi, H., Kuwajima, I., & Nojima, Y. (2008). Evolutionary multiobjective rule selection for classification rule mining. In A. Ghosh, S. Dehuri, & S. Ghosh (Eds.), Multi-objective evolutionary algorithms for knowledge discovery from databases. Berlin: Springer.Google Scholar
  35. 35.
    Little, J. D. C. (1992). Are there ‘Laws’ of manufacturing. In J. A. Heim, & W.D. Compton (Eds.), Manufacturing systems: Foundations of world-class practice. Washington, DC: National Academy Press (pp. 180–188).Google Scholar
  36. 36.
    Hopp, W. J., & Spearman, M. L. (2000). Factory physics: foundations of manufacturing management (2nd ed.). Burr Ridge, IL: Irwin McGraw-Hill Higher Education.Google Scholar
  37. 37.
    Spearman, M. L., Woodruff, D. L., & Hopp, W. J. (1990). CONWIP: A pull alternative to Kanban. International Journal of Production Research, 28(5), 879–894.CrossRefGoogle Scholar
  38. 38.
    Ng, A.H.C., Grimm, H., Lezama, T., Persson, A., Andersson, M., & Jägstam, M. (2008). OPTIMISE: An internet-based platform for metamodel-assisted simulation optimization. In: X. Huang, Y-S. Chen, & S-L. Ao (Eds), Recent Advances in Communication Systems and Electrical Engineering. Heidelberg: Springer (pp. 281–296).Google Scholar
  39. 39.
    Law, A. M., & Kelton, W. D. (2000). Simulation Modeling and Analysis (3rd ed.). New York: McGraw-Hill Higher Education.Google Scholar
  40. 40.
    Goldratt, E. M., & Cox, J. (1986). The goal: a process of ongoing improvement (revised edition ed.). Croton-on-Hudson, NY: North River Press.Google Scholar
  41. 41.
    Roser, C., Nakano, M., & Tanaka, M. (2002). Shifting bottleneck detection. Proceedings of the 2002 Winter Simulation Conference, San Diego, CA, USA (pp.1079–1086).Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Amos H. C. Ng
    • 1
  • Catarina Dudas
    • 1
  • Johannes Nießen
    • 1
  • Kalyanmoy Deb
    • 1
    • 2
  1. 1.Virtual Systems Research CentreUniversity of SkövdeSkövdeSweden
  2. 2.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia

Personalised recommendations