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Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction Read chapter Read first chapter

2011 | OriginalPaper | Chapter

15. Simulation-Based Innovization Using Data Mining for Production Systems Analysis

Authors : Amos H. C. Ng, Catarina Dudas, Johannes Nießen, Kalyanmoy Deb

Published in: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Publisher: Springer London

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Abstract

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.

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previous chapter A Simulation Optimisation Framework for Container Terminal Layout Design
next chapter Multi-objective Production Systems Optimisation with Investment and Running Cost
Footnotes
1
Cycle time, which is also called variously as manufacturing lead time, throughput time or sojourn time, is used in this paper to refer to the time from a job is released at the beginning of the line/system until it reaches its end (i.e., the time a part spends as WIP). This terminology follows the definition found in standard textbooks for manufacturing systems analysis, e.g., [36].
 
2
Defined in the shifting bottleneck detection method [41], active time is the time when the machine is working, changing tools or in repair and inactive time includes starving, blocked or waiting.
 
Literature
1.
Chryssolouris, G. (1992). Manufacturing systems: Theory and practice. New York: Springer.
2.
Wu, B. (1992). Manufacturing systems design and analysis (2nd ed.). London: Chapman and Hall.
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.CrossRef
4.
Goldratt, E. M. (1991). Haystack Syndrome. Great Barrington, MA: North River Press.
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).
6.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (3rd ed.). Wiltshire: Wiley.MATH
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.
8.
Bandaru, S., & Deb, K. (2010). Automating discovery of innovative design principles through optimization. KanGAL Technical Report No.2010001.
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.
10.
Rokach, L., & Maimon, O. Z. (2008). Data mining with decision trees: Theory and applications. Hackensack, NJ: World Scientific.MATH
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.CrossRef
12.
Han, J., & Kamber, M. (2004). Data mining: Concepts and techniques (7th ed.). San Francisco, Calif: Kaufmann.
13.
Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An Overview. AI Magazine, 13, 57–70.
14.
Vedder, A. (1999). KDD: The challenge to individualism. Ethics and Information Technology, 1, 275–281.CrossRef
15.
Sumathi, S., & Sivanandam, S. N. (2006). Introduction to data mining and its applications. Berlin: Springer.MATHCrossRef
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.
17.
18.
Weiss, S. M., & Indurkhya, N. (2002). Predictive data mining: A practical guide. San Francisco, CA: Morgan Kaufmann.
19.
Neckel, P., & Knobloch, B. (2005). Customer relationship analytics: Praktische anwendung des data mining im CRM (1st ed.). Heidelberg: dpunkt-Verl.
20.
21.
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). New York, NY: Wiley.MATH
22.
Groth, R. (1998). Data mining: a hands-on approach for business professionals. Upper Saddle River, NJ: Prentice-Hall.
23.
Bissantz, N., & Hagedorn, J. (2009). Data mining. Business & Information Systems Engineering, 1, 118–122.CrossRef
24.
Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38, 1–32.CrossRef
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.
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.CrossRef
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).
28.
Joseph, V. R., & Ying, H. (2008). Orthogonal-maximin Latin hypercube designs. Statistica Sinica, 18, 171–186.MathSciNetMATH
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).
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.
31.
Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1–2), 105–139.CrossRef
32.
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.MathSciNetCrossRef
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.
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).
36.
Hopp, W. J., & Spearman, M. L. (2000). Factory physics: foundations of manufacturing management (2nd ed.). Burr Ridge, IL: Irwin McGraw-Hill Higher Education.
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.CrossRef
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).
39.
Law, A. M., & Kelton, W. D. (2000). Simulation Modeling and Analysis (3rd ed.). New York: McGraw-Hill Higher Education.
40.
Goldratt, E. M., & Cox, J. (1986). The goal: a process of ongoing improvement (revised edition ed.). Croton-on-Hudson, NY: North River Press.
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).
Metadata
Title
Simulation-Based Innovization Using Data Mining for Production Systems Analysis
Authors
Amos H. C. Ng
Catarina Dudas
Johannes Nießen
Kalyanmoy Deb
Copyright Year
2011
Publisher
Springer London
Book
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Print ISBN: 978-0-85729-617-7

Electronic ISBN: 978-0-85729-652-8

Copyright Year: 2011

DOI
https://doi.org/10.1007/978-0-85729-652-8_15

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