Skip to main content
Log in

Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information

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

Abstract

Since discrete manufacturing system (DMS) is a complicated dynamic network that comprises of processes, machines, and work in process, a coherent methodology for performance tracking and sustainable improvement at the system/network level is of great significance for manufacturers to respond rapidly in a mass customization paradigm. Fortunately, the radio frequency identification (RFID) technologies provide us the real-time tracking ability of the production process that suffers unpredictable and recessive disturbances. This paper proposes a dynamic scheduling approach based on multi-layer network metrics of RFID-driven DMS. Firstly, considering the elements of DMS (e.g., parts, manufacturing activities, and equipment) and relationships among them, a DMS model named complex manufacturing network (CMN) is proposed. Then, several multi-layer network metrics of the CMN are defined and analysed. The implications of these metrics lead to a better understanding of the current status and performance of DMS. Thirdly, a dynamic scheduling algorithm using these metrics as heuristic information is proposed to solve multi-resources and independent-task DMS. Finally, a Printing Machine manufacturing system is chosen as an example to illustrate the feasibility of the proposed approach.

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

Access this article

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Åkerman, M., Fast-Berglund, Å., & Ekered, S. (2016). Interoperability for a dynamic assembly system. Procedia CIRP, 44(2016), 407–411.

    Article  Google Scholar 

  • Benjaafar, S., & Ramakrishnan, R. (1996). Modelling, measurement and evaluation of sequencing flexibility in manufacturing systems. International Journal of Production Research, 34(5), 1195–1220.

    Article  Google Scholar 

  • Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4), 175–308.

    Article  Google Scholar 

  • Braha, D., & Bar-Yam, Y. (2004). Information flow structure in large-scale product development organizational networks. Journal of Information Technology, 19(4), 244–253.

    Article  Google Scholar 

  • Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46(3), 436–443.

    Article  Google Scholar 

  • Cheng, H., & Chu, X. N. (2012). A network-based assessment approach for change impacts on complex product. Journal of Intelligent Manufacturing, 23(4), 1419–1431.

    Article  Google Scholar 

  • Cheng-Leong, A., Pheng, K. L., & Leng, G. R. K. (1999). IDEF*: A comprehensive modelling methodology for the development of manufacturing enterprise systems. International Journal of Production Research, 37(17), 3839–3858.

    Article  Google Scholar 

  • Chongwatpol, J., & Sharda, R. (2013). RFID-enabled track and traceability in job-shop scheduling environment. European Journal of Operational Research, 227(3), 453–463.

    Article  Google Scholar 

  • Cowling, P., & Johansson, M. (2002). Using real time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230–244.

    Article  Google Scholar 

  • Dai, Q., Zhong, R., Huang, G. Q., Qu, T., Zhang, T., Luo, T. Y., et al. (2012). Radio frequency identification-enabled real-time manufacturing execution system: A case study in an automotive part manufacturer. International Journal of Computer Integrated Manufacturing, 25(1), 51–65.

    Article  Google Scholar 

  • Ding, K., Jiang, P., Sun, P., & Wang, C. (2016). RFID-Enabled Physical Object Tracking in Process Flow Based on an Enhanced Graphical Deduction Modeling Method. IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi:10.1109/TSMC.2016.2558104 (published online).

  • Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.

    Article  Google Scholar 

  • Fu, Y., & Jiang, P. (2012). RFID based e-quality tracking in service-oriented manufacturing execution system. Chinese Journal of Mechanical Engineering, 25(5), 974–981.

    Article  Google Scholar 

  • Guy, D. (1989). GRAI approach to designing and controlling advanced manufacturing system in CIM environment. Advanced Information Technologies for Industrial Material Flow Systems (pp. 461–529). Berlin, Heidelberg: Springer.

  • Haupt, R. (1989). A survey of priority rule-based scheduling. Operations-Research-Spektrum, 11(1), 3–16.

    Article  Google Scholar 

  • Huang, G. Q., Fang, M. J., Lu, H., Dai, Q. Y., Liu, W., & Newman, S. (2009). RFID-enabled real-time masscustomized production planning and scheduling. Paper presented at the 19th international conference on flexible automation and intelligent manufacturing, 6–8 July 2009, Teesside

  • Huang, S. H., Dismukes, J. P., Shi, J., Su, Q., Wang, G., Razzak, M. A., et al. (2002). Manufacturing system modeling for productivity improvement. Journal of Manufacturing Systems, 21(4), 249–259.

    Article  Google Scholar 

  • Jiang, P., Leng, J., Ding, K., Gu, P., & Koren, Y. (2016). Social Manufacturing as a sustainable paradigm for mass individualization. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 210(10), 1961–1968.

    Article  Google Scholar 

  • Jiang, P., Zhang, C., Leng, J., & Zhang, J. (2015). Implementing a WebAPP-based Software Framework for Manufacturing Execution Systems. IFAC-PapersOnLine, 48(3), 388–393.

    Article  Google Scholar 

  • Khodke, P. M., & Bhongade, A. S. (2013). Real-time scheduling in manufacturing system with machining and assembly operations: A state of art. International Journal of Production Research, 51(16), 4966–4978.

    Article  Google Scholar 

  • Kito, T., & Ueda, K. (2014). The implications of automobile parts supply network structures: A complex network approach. CIRP Annals-Manufacturing Technology, 63(1), 393–396.

    Article  Google Scholar 

  • Leng, J., Jiang, P., & Ding, K. (2014). Implementing of a three-phase integrated decision support model for parts machining outsourcing. International Journal of Production Research, 52(12), 3614–3636.

    Article  Google Scholar 

  • Leng, J., Jiang, P., & Zheng, M. (2015). Outsourcer–supplier coordination for parts machining outsourcing under social manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. doi:10.1177/0954405415583883 (published online).

  • Leng, J., & Jiang, P. (2016). A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm. Knowledge-Based Systems, 100, 188–199.

    Article  Google Scholar 

  • Li, J. (2005). Overlapping decomposition: A system-theoretic method for modeling and analysis of complex manufacturing systems. IEEE Transactions on Automation Science and Engineering, 2(1), 40–53.

    Article  Google Scholar 

  • Li, J., Dai, X. Z., & Meng, Z. D. (2009). Automatic reconfiguration of Petri net controllers for reconfigurable manufacturing systems with an improved net rewriting system-based approach. IEEE Transactions on Automation Science and Engineering, 6(1si), 156–167.

  • Lin, Y. K., Chang, P. C., & Chen, J. C. (2013). Performance evaluation for a footwear manufacturing system with multiple production lines and different station failure rates. International Journal of Production Research, 51(5), 1603–1617.

  • Liu, D., & Jiang, P. (2009). The complexity analysis of a machining error propagation network and its application. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(6), 623–640.

  • Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling, 12(4), 417–431.

    Article  Google Scholar 

  • Peng, W., Huang, M., & Yongping, H. (2015). A multi-mode critical chain scheduling method based on priority rules. Production Planning & Control, 26(12), 1011–1024.

    Article  Google Scholar 

  • Qu, T., Yang, H. D., Huang, G. Q., Zhang, Y. F., Luo, H., & Qin, W. (2012). A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers. Journal of Intelligent Manufacturing, 23(6SI), 2343–2356.

    Article  Google Scholar 

  • Sabuncuoglu, I., & Bayiz, M. (2000). Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research, 126(3), 567–586.

    Article  Google Scholar 

  • Santarek, K., & Buseif, I. M. (1998). Modelling and design of flexible manufacturing systems using SADT and Petri nets tools. Journal of Materials Processing Technology, 76(1–3), 212–218.

    Article  Google Scholar 

  • Spencer, J. W. (2003). Global gatekeeping, representation, and network structure: A longitudinal analysis of regional and global knowledge-diffusion networks. Journal of International Business Studies, 34(5), 428–442.

    Article  Google Scholar 

  • Suri, R., & Otto, K. (1999). System-level robustness through integrated modeling. Proceedings of the 11th international conference on design theory and methodology (pp. 12–15).

  • Voorbraak, F. (1995). Combining unreliable pieces of evidence. Amsterdam: University of Amsterdam.

    Google Scholar 

  • Vrabic, R., Husejnagic, D., & Butala, P. (2012). Discovering autonomous structures within complex networks of work systems. CIRP Annals-Manufacturing Technology, 61(1), 423–426.

    Article  Google Scholar 

  • Wang, C., & Jiang, P. (2016). Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops. Journal of Intelligent Manufacturing. doi:10.1007/s10845-016-1194-1 (published online).

  • Xu, X. W., Wang, L., & Rong, Y. (2006). STEP-NC and function blocks for interoperable manufacturing. IEEE Transactions on Automation Science and Engineering, 3(3), 297–308.

    Article  Google Scholar 

  • Yang, K. (1998). A comparison of dispatching rules for executing a resource-constrained project with estimated activity durations. Omega, 26(6), 729–738.

    Article  Google Scholar 

  • Zhang, F. Q., Jiang, P. Y., Zheng, M., & Cao, W. (2013). A performance evaluation method for radio frequency identification-based tracking network of job-shop-type work-in-process material flows. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227(10), 1541–1557.

    Article  Google Scholar 

  • Zhang, Y., Jiang, P., Huang, G., Qu, T., Zhou, G., & Hong, J. (2012). RFID-enabled real-time manufacturing information tracking infrastructure for extended enterprises. Journal of Intelligent Manufacturing, 23(6SI), 2357–2366.

    Article  Google Scholar 

  • Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Zhang, T., & Xu, C. (2015). A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing. Advanced Engineering Informatics, 29(2015), 799–812.

    Article  Google Scholar 

  • Zhong, R. Y., Li, Z., Pang, L. Y., Pan, Y., Qu, T., & Huang, G. Q. (2013). RFID-enabled real-time advanced planning and scheduling shell for production decision making. International Journal of Computer Integrated Manufacturing, 26(7), 649–662.

    Article  Google Scholar 

  • Zhou, G., Zheng, M., & Xiao, Z. (2011). Dynamic job rescheduling using RFID technology. International Journal of Internet Manufacturing and Services, 3(1), 42–58.

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation of China under Grant Nos. 71571142 and 51275396.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pingyu Jiang.

Appendix

Appendix

See Table 6.

Table 6 Related hardware infrastructure in the implementation

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leng, J., Jiang, P. Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information. J Intell Manuf 30, 979–994 (2019). https://doi.org/10.1007/s10845-017-1301-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1301-y

Keywords

Navigation