Integrating Data Mining and Simulation Optimization for Decision Making in Manufacturing
Manufacturers are facing an ever-increasing demand for customized products on the one hand and environmentally friendly products on the other. This situation affects both the product and the process life cycles. To guide decision-making across these life cycles, the performance of today’s manufacturing systems is monitored by collecting and analyzing large volumes of data, primarily from the shop floor. A new research field, Data Mining, can uncover insights hidden in that data. However, insights alone may not always result in actionable recommendations. Simulation models are frequently used to test and evaluate the performance impacts of various decisions under different operating conditions. As the number of possible operating conditions increases, so does the complexity and difficulty to understand and assess those impacts. This chapter describes a decision-making methodology that combines data mining and simulation. Data mining develops associations between system and performance to derive scenarios for simulation inputs. Thereafter, simulation is used in conjunction with optimization is to produce actionable recommendations. We demonstrate the methodology with an example of a machine shop where the concern is to optimize energy consumption and production time. Implementing this methodology requires interface standards. As such, this chapter also discusses candidate standards and gaps in those standards for information representation, model composition, and system integration.
KeywordsData Mining Machine Tool Association Rule Unify Modeling Language Production Time
This effort has been sponsored in part under the cooperative agreement No. 70NANB13H153 between NIST and Morgan State University. The work described was funded by the United States Government and is not subject to copyright. Qais Y. Hatim, formerly a Guest Researcher at the National Institute of Standards and Technology, contributed to the research of this chapter.
Disclaimer No approval or endorsement of any commercial product by the National Institute of Standards and Technology is intended or implied. Certain commercial software systems are identified in this paper to facilitate understanding. Such identification does not imply that these software systems are necessarily the best available for the purpose.
- 1.AEI (American Enterprise Institute). http://www.aei.org.
- 2.Kibira, D., Jain, S., & McLean, C. (2009). A system dynamics modeling framework for sustainable manufacturing. In Proceedings of the 27th Annual System Dynamics Society Conference, July 26–30, Albuquerque, NM. http://www.systemdynamics.org/conferences/2009/proceed/papers/P1285.pdf.
- 3.ISO/IEC 9075-1. Information Technology—Database Languages—SQL—Part 1: Framework (SQL/Framework). http://www.iso.org/iso/catalogue_detail.htm?csnumber=45498.
- 4.UNECE (United Nations Economic Commission for Europe). http://www.unece.org.
- 5.Gröger, C., Niedermann, F., Schwarz, H., & Mitschang, D. (2012). Supporting manufacturing design by analytics: Continuous collaborative process improvement enabled by the advanced manufacturing analytics platform. In Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work. Google Scholar
- 8.Brady, T., & Yellig, E. (2005). Simulation data mining: A new form of computer simulation output. In M. E. Kuhl, N. M. Steiger, F. B. Armstrong, & J. A. Joines (Eds.), Proceedings of the 2005 Winter Simulation Conference (pp. 285–289). New Jersey: Institute of Electrical and Electronics Engineers Inc.CrossRefGoogle Scholar
- 9.Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, AI Magazine, 17(3), 17–54.Google Scholar
- 10.Agard, B., & Kusiak, A. (2005). Data mining in selection of manufacturing processes. In O. Maimon & L. Rokach (Eds.), The data mining and knowledge discovery handbook (pp. 1159–1166). Springer.Google Scholar
- 12.Agrawal. R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In J. B. Bocca, M. Jarke, & C. Zaniolo (Eds.), Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Conference, Santiago, Chile (pp. 487–99).Google Scholar
- 14.Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, I. (1996). Fast discovery of association rules. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining. Cambridge, Mass: AAAI/MIT Press.Google Scholar
- 15.Ng, M. A. (2003). Parallel tabu search heuristic for clustering data sets. In Presented at the International Conference on Parallel Processing Workshops (ICPPW’’03), Kaohsiung, Taiwan.Google Scholar
- 16.Kerdprasop, K., & Kerdprasop, N. (2013). Cluster-based sequence analysis of complex manufacturing process. In Proceedings of the International Multi-Conference of Engineers and Computer Scientists, IMECS 2013, Hong Kong (Vol 1).Google Scholar
- 17.Kelton, D. W., R. P. Sadowski, & Swets, N. B. (2010). Simulation with Arena (5th ed.). McGraw-Hill Book Company, International Edition.Google Scholar
- 18.Law, A., & Kelton, D. (2007). Simulation modeling and analysis (2nd ed.). McGraw-Hill, International Editions.Google Scholar
- 20.Rogers, P., & Gordon, R. J. (1993). Simulation for real-time decision making in manufacturing systems. In G. W. Evans, M. Mollaghaseni, E. C. Russell, & W. E. Biles (Eds.), Proceedings of the 1993 Winter Simulation Conference (pp. 866–874).Google Scholar
- 22.Skoogh, A., Michaloski, J., & Bengtsson, N. (2010). Towards continuously updated simulation models: Combining automated raw data collection and automated data processing. In B. Johansson, S. Jain, J. Montoya-Torres, & E. Yucesan (Eds.), Proceedings of the 2010 Winter Simulation Conference (pp. 1678–1689).Google Scholar
- 23.Shao, G., Shin, S. -J., & Jain, S. (2014). Data analytics using simulation for smart manufacturing, In A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, & J. A. Miller (Eds.), Proceedings of the 2014 Winter Simulation Conference (pp. 2192–2203).Google Scholar
- 24.Azadivar. F. (1999) Simulation optimization methodologies. In P. A. Farrington, H. B. Nembhard, D. T. Sturrock, & G. W. Evans (Eds.), Proceedings of the 1999 Winter Simulation Conference (pp. 93–100).Google Scholar
- 25.Brady, T., & Bowden, R. (2001). The effectiveness of generic optimization routines in computer simulation languages, In Proceedings of the Industrial Engineering Research Conference, Dallas, Texas.Google Scholar
- 27.Carson, Y., & Maria, A. (1997). Simulation optimization: methods and applications. In S. Andradottir, K. J. Healy, D. H. Withers, & B. L. Nelson (Eds.), Proceedings of the 1997 Winter Simulation Conference (pp. 118–126). Piscataway, New Jersey: Institute of Electrical and Electronics Engineers Inc.Google Scholar
- 28.Fu, M., Glover, F. W., & April, J. (2005). Simulation optimization: A review, new developments, and applications. In M. E. Kuhl, N. M. Steiger, F. B. Armstrong, & J. A. Joines (Eds.), Proceedings of the 2005 Winter Simulation Conference.Google Scholar
- 29.Phatak, S. J. Venkateswaran, G. Pandey, S. Sabnis, & Pingle, A. (2014). Simulation based optimization using PSO in manufacturing flow problems: A case study. In A. Tolk, S. D. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, & J. A. Miller (Eds.), Proceedings of the 2014 Winter Simulation Conference (pp. 2136–2146).Google Scholar
- 30.Remondino, M., & Correndo, G. (2005). Data mining applied to agent based simulation. In Proceedings of the 19th European Conference on Modeling and Simulation, Riga, Latvia.Google Scholar
- 31.Bogon, T., Timm, I. J., Lattner, A. D., Paraskevopoulos, D., Jessen, U., Schmitz, M., et al. (2012). Towards assisted input and output data analysis in manufacturing simulation: The EDASim approach. In C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, & A. M. Uhrmacher (Eds.), Proceedings of the 2012 Winter Simulation Conference (pp. 257–269). Piscataway, New Jersey: Institute of Electrical and Electronics Engineers Inc.Google Scholar
- 32.Dudas, C. (2014). Learning from multi-objective optimization of production systems: A method for analyzing solution sets from multi-objective optimization. PhD Thesis, Stockholm University, Sweden.Google Scholar
- 33.Friedenthal, S., Moore, A., & Steiner, R. (2015). A practical guide to SysML: The systems modeling language (3rd ed.). Elsevier Inc: Morgan Kaufmann.Google Scholar
- 34.MTConnect Part 1. (2011). The Association for Manufacturing Technology, “Getting Started with MTConnect: Connectivity Guide”, White Paper, MTConnect.Google Scholar
- 35.SISO (Simulation Interoperability Standards Organization). Core Manufacturing Simulation Data (CMSD) Standard. https://www.sisostds.org/DesktopModules/Bring2mind/DMX/Download.aspx?Command=Core_Download&EntryId=36239&PortalId=0&TabId=105.
- 36.Kibira, D., Hatim, Q., Kumara, S., & Shao, G. (2015). Integrating data analytics and simulation methods to support manufacturing decision making. In L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, & M. D. Rossetti (Eds.), Proceedings of the 2015 Winter Simulation Conference (pp. 2100–2111).Google Scholar
- 38.Zhao, M., & Zhu, S. (1995). Mechanical engineering handbook. Beijing, China: China Machine Press.Google Scholar
- 39.Iwata, K. (1972). A probabilistic approach to the determination of the optimum cutting conditions. Journal of Manufacturing Science and Engineering, 94(4), 1099–1107.Google Scholar
- 42.Congbo, L., Ying, T., Longguo, C., & Pengyu, L. (2013). A quantitative approach to analyze carbon emissions of CNC-based machining systems. Journal of Intelligent Manufacturing, 4, 34–46.Google Scholar
- 45.Rakotomalala, R. (2005). TANAGRA: A free software for research and academic purposes. In Proceedings of European Grid Conference 2005, RNTI-E-3 (Vol. 2, pp. 697–702). Amsterdam.Google Scholar
- 47.AMT. (2013). Getting Started with MTConnect: Monitoring Your Shop Floor—What’s In It For You?, AMT—The Association for Manufacturing Technology. Retrieved February 2, 2015, from http://www.mtconnect.org/media/39437/gettingstartedwithmtconnectshopfloormonitoringwhatsinitforyourevapril4th-2013.pdf.
- 48.SISO. (2012). SISO-STD-008-01-2012: Standard for Core Manufacturing Simulation Data—XML Representation. Orlando, F L: Simulation Interoperability Standards Organization.Google Scholar
- 49.ANSI/ISA 95. ANSI/ISA–95.00.03–2005—Enterprise-control system integration: Part 3: Activity models of manufacturing operations management. Google Scholar
- 50.OAGi. Open Application Group’s Integration Specification (OAGIS). (2014). http://www.oagi.org/dnn2/DownloadsandResources/OAGIS100PublicDownload.aspx, Inc.