Skip to main content
Springer Nature Link
Log in

Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Radio frequency identification (RFID) has been widely used in manufacturing field and created a ubiquitous production environment, where advanced production planning and scheduling (APS) might be enabled. Within such environment, APS usually requires standard operation times (SOTs) and dispatching rules which have been obtained from time studies or based on past experiences. Wide variations exist and frequently cause serious discrepancies in executing plans and schedules. This paper proposes a data mining approach to estimate realistic SOTs and unknown dispatching rules from RFID-enabled shopfloor production data. The approach is evaluated by real-world data from a collaborative company which has been used RFID technology for supporting its shopfloor production over seven years. The key impact factors on SOTs are quantitatively examined. A reference table with the mined precise and practical SOTs is established for typical operations and suitable dispatching rules are labled as managerial implicities, aiming at improving the quality and stability of production plans and schedules.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Al-Mashari, M., Al-Mudimigh, A., et al. (2003). Enterprise resource planning: A taxonomy of critical factors. European Journal of Operational Research, 146(2), 352–364.

    Article  Google Scholar 

  • Banaszak, Z. A., Skolud, B. Ż., et al. (2003). Computer-aided prototyping of production flows for a virtual enterprise. Journal of Intelligent Manufacturing, 14(1), 83–106.

    Article  Google Scholar 

  • Brintrup, A., Ranasinghe, D., & McFarlane, D. (2010). RFID opportunity analysis for leaner manufacturing. International Journal of Production Research, 48(9), 2745–2764.

    Article  Google Scholar 

  • Cakici, O. E., Groenevelt, H., et al. (2011). Using RFID for the management of pharmaceutical inventory-system optimization and shrinkage control. Decision Support Systems, 51(4), 842–852.

    Article  Google Scholar 

  • Chen, R. S., & Tu, M. A. (2009). Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology. Expert Systems with Applications, 36(4), 7581–7593.

    Google Scholar 

  • Choudhary, A., Harding, J., et al. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521.

    Article  Google Scholar 

  • Chu, H. J., Liau, C. J., et al. (2012). Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region. Expert Systems with Applications, 39(10), 9451–9457.

    Article  Google Scholar 

  • Ding, Z. M., Xu, J. J., et al. (2012). SeaCloudDM: a database cluster framework for managing and querying massive heterogeneous sensor sampling data. The Journal of Supercomputing. doi:10.1007/s11227-012-0762-1.

  • Fayyad, U., Piatetsky-Shapiro, G., et al. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.

    Google Scholar 

  • Fayyad, U. M., Piatetsky-Shapiro, G., et al. (1996). Advances in knowledge discovery and data mining. Cambridge: The MIT Press.

    Google Scholar 

  • Ferrer, G., Heath, S., et al. (2011). An RFID application in large job shop remanufacturing operations. International Journal of Production Economics, 133(2), 612–621.

    Article  Google Scholar 

  • Frank, J. (2007). Using data mining to enhance automated planning and scheduling. In Proceedings of IEEE symposium on computational intelligence and data mining (pp. 251–260), Honolulu, HI, IEEE, March 1–April 5.

  • Günther, H. O., & van Beek, P. (2003). Advanced planning and scheduling solutions in process industry. Berlin: Springer.

    Book  Google Scholar 

  • Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: A tutorial. Analytica Chimica Acta, 185, 1–17.

    Article  Google Scholar 

  • Gonzalez, H., Han, J. W., et al. (2010). Modeling massive RFID data sets: A gateway-based movement graph approach. IEEE Transactions on Knowledge and Data Engineering, 22(1), 90–104.

    Article  Google Scholar 

  • Guo, Z. X., Wong, W. K., & Leung, S. Y. S. (2012). A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Applied Soft Computing (in press). https://www.e-proof.sps.co.in/springer/ja.asp?rfp=authvltvlatmel.

  • Guo, Z. X., Wong, W. K., et al. (2009). Intelligent production control decision support system for flexible assembly lines. Expert Systems with Applications, 36(3), 4268–4277.

    Article  Google Scholar 

  • Gupta, A. (2000). Enterprise resource planning: The emerging organizational value systems. Industrial Management & Data Systems, 100(3), 114–118.

    Article  Google Scholar 

  • Han, J. W., Gonzalez, H., et al. (2006). Warehousing and mining massive RFID data sets. Advanced Data Mining and Applications, 4093(2006), 1–18.

    Article  Google Scholar 

  • Han, J. W., Kamber, M., et al. (2011). Data mining: Concepts and techniques. Los Altos: Morgan Kaufmann.

    Google Scholar 

  • Han, J. W., Li, Z. H., et al. (2010). Mining moving object, trajectory and traffic data. Database Systems for Advanced Application—Lecture Notes in Computer Science, 5982(2010), 485–486.

    Google Scholar 

  • Hax, A. C., & Meal, H. C. (1973). Hierarchical integration of production planning and scheduling. Massachusetts Institute of Technology (MIT), Sloan School of Management: DTIC Document. http://hdl.handle.net/1721.1/1868.

  • Hu, M., Chen, Y., et al. (2009). Building sparse multiple-kernel SVM classifiers. IEEE Transactions on Neural Networks, 20(5), 827–839.

    Article  Google Scholar 

  • Huang, G. Q., Zhang, Y. F., & Jiang, P. Y. (2007). RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts. Robotics and Computer-Integrated Manufacturing, 23(4), 469–477.

    Article  Google Scholar 

  • Huang, G. Q., Fang, J. et al. (2009). RFID-Enabled Real-Time Mass-Customized Production Planning and Scheduling. Proceedings of 19th international conference on flexible automation and intelligent manufacturing, 6–8 July, Teesside, UK.

  • Huang, G. Q., Wright, P. K., et al. (2009). Wireless manufacturing: A literature review, recent developments, and case studies. International Journal of Computer Integrated Manufacturing, 22(7), 579– 594.

    Article  Google Scholar 

  • Ivert, L. K., & Jonsson, P. (2010). The potential benefits of advanced planning and scheduling systems in sales and operations planning. Industrial Management & Data Systems, 110(5), 659–681.

    Article  Google Scholar 

  • Jeffery, S. R., Garofalakis, M., et al. (2006). Adaptive cleaning for RFID data streams. Proceedings of the 32nd International Conference on Very Large Databases, (pp. 163–174), 12–15 Sep. Korea: Seoul.

  • Joachims, T. (1999). Making large scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, & A. J. Smola (Eds.), Advances in kernel methods—support vector learning (pp. 169–184). Cambridge, MA: MIT Press.

  • Johnson, D. (2002). RFID tags improve tracking, quality on Ford line in Mexico. Control Engineering, Nov. 1, 2002.

  • Kallrath, J. (2002). Planning and scheduling in the process industry. OR spectrum, 24(3), 219–250.

    Article  Google Scholar 

  • Khoshnevis, B., & Chen, Q. M. (1991). Integration of process planning and scheduling functions. Journal of Intelligent Manufacturing, 2(3), 165–175.

    Article  Google Scholar 

  • Kochar, B., & Chhillar, R. S. (2011). A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques. International Journal of Wireless Information Networks, 18(4), 309–318.

    Article  Google Scholar 

  • Kusiak, A., & Smith, M. (2007). Data mining in design of products and production systems. Annual Reviews in Control, 31(1), 147–156.

    Article  Google Scholar 

  • Makris, S., Michalos, G., & Chryssolouris, G. (2011). RFID driven robotic assembly for random mix manufacturing. Robotics and Computer-Integrated Manufacturing, 28(3), 359–365.

    Article  Google Scholar 

  • Neumann, K., Schwindt, C., et al. (2002). Advanced production scheduling for batch plants in process industries. OR spectrum, 24(3), 251–279.

    Article  Google Scholar 

  • Ngai, E. W. T., Moon, K. K. L., et al. (2008). RFID research: An academic literature review (1995–2005) and future research directions. International Journal of Production Economics, 112(2), 510–520.

    Google Scholar 

  • Ozturk, A., Kayaligil, S., et al. (2006). Manufacturing lead time estimation using data mining. European Journal of Operational Research, 173(2), 683–700.

    Article  Google Scholar 

  • Poon, T. C., Choy, K. L., Chan, F. T. S., & Lau, H. C. W. (2011). A real-time production operations decision support system for solving stochastic production material demand problems. Expert Systems with Applications, 38(5), 4829–4838.

    Article  Google Scholar 

  • Rao, J., Doraiswamy, S., et al. (2006). A deferred cleansing method for RFID data analytics. In Proceeding of the 32nd International Conference on Very Large Databases, (pp. 175–186), 12–15 Sep, Seoul, Korea. VLDB Endowment.

  • Saygin, C., & Tamma, S. (2012). RFID-enabled shared resource management for aerospace maintenance operations: A dynamic resource allocation model. International Journal of Computer Integrated Manufacturing, 25(1), 100–111.

    Article  Google Scholar 

  • Shao, X. Y., Li, X. Y., et al. (2009). Integration of process planning and scheduling—a modified genetic algorithm-based approach. Computers & Operations Research, 36(6), 2082–2096.

    Article  Google Scholar 

  • Shen, W., Wang, L., et al. (2006). Agent-based distributed manufacturing process planning and scheduling: A state-of-the-art survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 36(4), 563–577.

    Article  Google Scholar 

  • Tan, W., & Khoshnevis, B. (2000). Integration of process planning and scheduling—a review. Journal of Intelligent Manufacturing, 11(1), 51–63.

    Article  Google Scholar 

  • Vollmann, T. E., Berry, W. L., et al. (1997). Manufacturing planning and control systems. Homewood: Irwin.

    Google Scholar 

  • Wang, K. (2007). Applying data mining to manufacturing: The nature and implications. Journal of Intelligent Manufacturing, 18(4), 487–495.

    Article  Google Scholar 

  • Wang, W. L., Chang, C. P. et al. (2009). A RFID-enabled with data mining model for exhibition industry. In Proceeding of the 6th International Conference on Service Systems and Service Management, (pp. 664–668). Xiamen, China: IEEE, June 8–10.

  • Wang, B., Cao, Z., Yan, Y., Liu, W., & Wang, Z. (2011). Fundamental technology for RFID-based supervisory control of shop floor production system. The International Journal of Advanced Manufacturing Technology, 57(9–12), 1–19.

    Google Scholar 

  • Yeh, K. C., Chen, R. S., & Chen, C. C. (2011). Intelligent service-integrated platform based on the RFID technology and software agent system. Expert Systems with Applications, 38(4), 3058–3068.

    Article  Google Scholar 

  • Zhang, Y. F., Qu, T., et al. (2011). Real-time work-in-progress management for smart object-enabled ubiquitous shop-floor environment. International Journal of Computer Integrated Manufacturing, 24(5), 431–445.

    Google Scholar 

  • Zhong, R. Y., Huang, G. Q., Dai, Q. Y., Zhang, T. (2012). Estimation of lead time in the RFID-enabled real-time shopfloor production with a data mining model. In Proceeding of The 19th international conference on industrial engineering and engineering management, Oct 27–29, Changsha, China.

  • Zhong, R. Y., Dai, Q. Y., Qu, T., Hu, G. J., & Huang, G. Q. (2013). RFID-enabled Real-time Manufacturing Execution System for Mass-customization Production. Robotics and Computer-Integrated Manufacturing, 29(2), 283–292.

    Article  Google Scholar 

Download references

Acknowledgments

Authors would like to acknowledge 2009 Guangdong Modern Information Service Fund (GDIID2009IS048), 2010 Guangdong Department of Science and Technology Funding (2010B050100023), National Natural Science Foundation of China (61074146), Key Laboratory of Internet of Manufacturing Things Technology and Engineering of Development and Reform Commission of Guangdong Province, and International Collaborative Project of Guangdong High Education Institution (gjhz1005).

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China

    Ray Y. Zhong & George Q. Huang

  2. School of Information Engineering, Guangdong University of Technology, Guangzhou, China

    Q. Y. Dai

  3. Huaiji Dengyun Auto-parts (Holding) Co., Ltd., Huaiji, Zhaoqing, Guangdong, China

    T. Zhang

Authors
  1. Ray Y. Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Q. Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Q. Y. Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George Q. Huang.

Appendix

Appendix

See Tables 8, 9 and 10.

Table 8 Data samples for mining SOTs
Full size table
Table 9 RFID-enabled dispatching list
Full size table
Table 10 SOTs for typical processes under specific product
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhong, R.Y., Huang, G.Q., Dai, Q.Y. et al. Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data. J Intell Manuf 25, 825–843 (2014). https://doi.org/10.1007/s10845-012-0721-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0721-y

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy