Abstract
Cloud manufacturing (CMfg) promotes a dynamic distributed manufacturing environment by connecting the service providers and manages them in a centralized way. Due to the distinct production capabilities, the service providers tend to be delegated services of different granularities. Meanwhile, users of different types may be after services of different granularities. A traditional aggregate production planning method is often incapable of dealing with type of problems. For this reason, a multi-level aggregate service planning (MASP) methodology is proposed. The MASP service hierarchy is presented, which integrates the services of different granularities into a layered structure. Based on this structure, one of data mining technologies named time series is introduced to provide dynamic forecast for each layer. In this way, MASP can not only deal with the services of multi-granularity, but also meet the requirements of all related service providers irrespective of their manufacturing capabilities. A case study has been carried out, showing how MASP can be applied in a CMfg environment. The results of the prediction are considered reliable as the order of magnitude of the production for each service layer is much greater than that of the corresponding mean forecast error.
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References
Adamson, G., Wang, L., & Holm, M. (2013). The state of the art of cloud manufacturing and future trends. In ASME 2013 international manufacturing science and engineering conference collocated with the 41st North American manufacturing research conference (pp. V002T02A004). ASME.
Adhikari, R., & Agrawal, R. K. (2013). An introductory study on time series modeling and forecasting. Saarbrucken: LAP LAMBERT Academic Publishing.
Aliev, R. A., Fazlollahi, B., Guirimov, B. G., & Aliev, R. R. (2007). Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Information Sciences, 177(20), 4241–4255.
Chien, C. F., Hsiao, C. W., Meng, C., Hong, K. T., & Wang, S. T. (2005). Cycle time prediction and control based on production line status and manufacturing data mining. In ISSM 2005, IEEE international symposium on semiconductor manufacturing, 2005 (pp. 327–330).
Ding, B., Yu, X. Y., & Sun, L. J. (2012). A cloud-based collaborative manufacturing resource sharing services. Information Technology Journal, 11(9), 1258–1264.
Han, J., & Kamber, M. (2006). Data mining concepts and techniques. Amsterdam: Elsevier.
Holt, C. C., Modigliani, F., & Simon, H. A. (1955). A linear decision rule for production and employment scheduling. Management Science, 2(1), 1–30.
Jiao, J., Zhang, L., & Pokharel, S. (2007). Process platform planning for variety coordination from design to production in mass customization manufacturing. IEEE Transactions on Engineering Management, 54(1), 112–129.
Khakdaman, M., Wong, K. Y., Zohoori, B., Tiwari, M. K., & Merkert, R. (2015). Tactical production planning in a hybrid Make-to-Stock-Make-to-Order environment under supply, process and demand uncertainties: A robust optimisation model. International Journal of Production Research, 53(5), 1358–1386.
Laili, Y., Tao, F., Zhang, L., Cheng, Y., Luo, Y., & Sarker, B. R. (2013). A Ranking Chaos Algorithm for dual scheduling of cloud service and computing resource in private cloud. Computers in Industry, 64(4), 448–463.
Laili, Y., Zhang, L., & Tao, F. (2011). Energy adaptive immune genetic algorithm for collaborative design task scheduling in cloud manufacturing system. In IEEE international conference on industrial engineering and engineering management (IEEM), 2011 (pp. 1912–1916).
Lartigau, J., Nie, L., Xu, X., Zhan, D., & Mou, T. (2012). Scheduling methodology for production services in cloud manufacturing. In International joint conference on service sciences (IJCSS), 2012 (pp. 34–39).
Li, B. H., Zhang, L., Wang, S. L., Tao, F., Cao, J. W., Jiao, X. D., et al. (2010). Cloud manufacturing: A new service-oriented networked manufacturing model. Computer Integrated Manufacturing Systems, 16(1), 1–7.
Li, C. Q., Hu, C. Y., Wang, Y. W., & Zhu, P. F. (2011). Research of cloud manufacturing and resource encapsulation technology. Applied Mechanics and Materials, 58, 562–566.
Liu, I., & Jiang, H. (2012). Research on key technologies for design services collaboration in cloud manufacturing. In IEEE 16th international conference on computer supported cooperative work in design (CSCWD), 2012 (pp. 824–829).
Lu, Y., Xu, X., & Xu, J. (2014). Development of a hybrid manufacturing cloud. Journal of Manufacturing Systems, 33(4), 551–566.
Nahmias, S. (2001). Production and operations analysis. Beijing: Tsinghua University Press.
Olafsson, S., Li, X., & Wu, S. (2008). Operations research and data mining. European Journal of Operational Research, 187(3), 1429–1448.
Overmeyer, L., Dreyer, J., & Altmann, D. (2010). Data mining based configuration of cyclically interlinked production systems. CIRP Annals-Manufacturing Technology, 59(1), 493–496.
Sanderson, S., & Uzumeri, M. (1995). Managing product families: The case of the Sony Walkman. Research Policy, 24(5), 761–782.
Spohrer, J., Vargo, S. L., Caswell, N., & Maglio, P. P. (2008). The service system is the basic abstraction of service science. In Hawaii international conference on system sciences, proceedings of the 41st annual (pp. 104–104).
Tirkel, I. (2013). Forecasting flow time in semiconductor manufacturing using knowledge discovery in databases. International Journal of Production Research, 51(18), 5536–5548.
Wang, R. C., & Liang, T. F. (2005). Applying possibilistic linear programming to aggregate production planning. International Journal of Production Economics, 98(3), 328–341.
Wang, T., Guo, S., & Lee, C. G. (2014). Manufacturing task semantic modeling and description in cloud manufacturing system. The International Journal of Advanced Manufacturing Technology, 71(9–12), 2017–2031.
Wang, X. V., & Xu, X. W. (2013). An interoperable solution for cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 29(4), 232–247.
Wang, X. V., & Xu, X. W. (2013). Virtual function block mechanism in the cloud manufacturing environment. Advanced Materials Research, 694, 2438–2441.
Wu, D., Greer, M. J., Rosen, D. W., & Schaefer, D. (2013). Cloud manufacturing: Strategic vision and state-of-the-art. Journal of Manufacturing Systems, 32(4), 564–579.
Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2014). Cloud-based manufacturing: Old wine in new bottles? Procedia CIRP, 17(1), 94–99.
Xiang, F., & Hu, Y. F. (2012). Cloud manufacturing resource access system based on internet of things. Applied Mechanics and Materials, 121, 2421–2425.
Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75–86.
Yu, C., Xu, X., & Lu, Y. (2015). Computer-integrated manufacturing, cyber-physical systems and cloud manufacturing-concepts and relationships. Manufacturing Letters, 6, 5–9.
Zhang, L., Luo, Y., Tao, F., Li, B. H., Ren, L., Zhang, X., et al. (2014). Cloud manufacturing: A new manufacturing paradigm. Enterprise Information Systems, 8(2), 167–187.
Zhang, Y., Zhang, G., Liu, Y., & Hu, D. (2015). Research on services encapsulation and virtualization access model of machine for cloud manufacturing. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1064-2.
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The authors acknowledge the National Natural Science Foundation of China (No. 51275456), the National Key Technology R&D Program of China (No. 2013BAF02B00), and the financial support from the China Scholarship Council.
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Yu, C., Zhang, W., Xu, X. et al. Data mining based multi-level aggregate service planning for cloud manufacturing. J Intell Manuf 29, 1351–1361 (2018). https://doi.org/10.1007/s10845-015-1184-8
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DOI: https://doi.org/10.1007/s10845-015-1184-8