Weitere Artikel dieser Ausgabe durch Wischen aufrufen
In the traditional order completion time (OCT) prediction methods, some mutable and ideal production data (e.g., the arrival time of work in process (WIP), the planned processing time of all operations, and the expected waiting time per operation) are often used. Thus, the prediction time always deviates from the actual completion time dramatically even though the dynamicity of the production capacity and the real-time load conditions of job shop are considered in the OCT prediction method. On account of this, a new prediction method of OCT using the composition of order and real-time job shop RFID data is proposed in this article. It applies accurate RFID data to depict the real-time load conditions of job shop, and attempts to mine the mapping relationship between RFID data and OCT from historical data. Firstly, RFID devices capture the types and waiting list information of all WIPs which are in the in-stocks and out-stocks of machining workstations, and the real-time processing progress of all WIPs which are under machining at machining workstations. Secondly, a description model of real-time job shop load conditions is put forward by using the RFID data. Next, the mapping model based on the composition of order and real-time RFID data is established. Finally, deep belief network, which is one of the major technologies of deep neural networks, is applied to mine the mapping relationship. To illustrate the advantages of the proposed method, a numerical experiment compared with back-propagation (BP) network based prediction method, multi-hidden-layers BP network based prediction method and the principal components analysis and BP network based prediction method is conducted at last.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems, 19, 153–160.
Brahimi, N., Aouam, T., & Aghezzaf, E. (2014). Integrating order acceptance decisions with flexible due dates in a production planning model with load-dependent lead times. International Journal of Production Research, 53(12), 3810–3822. CrossRef
Enns, S. T. (1995). A dynamic forecasting model for job shop flowtime prediction and tardiness control. International Journal of Production Research, 33(5), 1295–1312. CrossRef
Gordon, V. S., & Strusevich, V. A. (1999). Earliness penalties on a single machine subject to precedence constraints: SLK due date assignment. Computers and Operations Research, 26(2), 157–177. CrossRef
Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8), 1771–1800. CrossRef
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. CrossRef
Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. CrossRef
Hopp, W. J., & Melanie, R. S. (2001). A simple, robust leadtime-quoting policy. Manufacturing and Service Operations Management, 3(4), 321–336. CrossRef
Hu, S., Zhang, B., & Zhang, X. (2012). Order completion date estimation and due date decision under make-to-order mode. Industrial Engineering Journal, 15(3), 122–129.
Keshmiri, S., Zheng, X., Chew, C. M., & Pang, C. K. (2015). Application of deep neural network in estimation of the weld bead parameters. arXiv:1502.4187.
Lawrence, R. S. (1995). Estimating flowtimes and setting due-dates in complex production systems. IIE Transactions, 27(5), 657–668. CrossRef
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Paper presented at the proceedings of the 26th annual international conference on machine learning.
Li, M., Yao, L., Yang, J., & Wang, Z. (2015). Due date assignment and dynamic scheduling of one-of-a-kind assembly production with uncertain processing time. International Journal of Computer Integrated Manufacturing, 28(6), 616–627. CrossRef
Liang, F., Fung, R. Y., & Jiang, Z. (2013). A comined approach of cycle time estimation in mass customization enterprise. International Journal of Industrial Engineering, 20(9), 574–588.
Lopes, N., & Ribeiro, B. (2015). Deep belief networks (DBNs). In J. Kacprzyk (Ed.), Machine learning for adaptive many-core machines–a practical approach (pp. 155–186). Switzerland: Springer.
Moses, S., Grant, H., Gruenwald, L., & Pulat, S. (2004). Real-time due-date promising by build-to-order environments. International Journal of Production Research, 42(20), 4353–4375. CrossRef
Okubo, H., Weng, J., Kaneko, R., & Simizu, T. (2000). Production lead-time estimation system based on neural network. Research paper.
Sabuncuoglu, I., & Comlekci, A. (2002). Operation-based owtime estimation in a dynamic job shop. Omega, 30(6), 423–442. CrossRef
Sarikaya, R., Hinton, G. E., & Ramabhadran, B. (2011). Deep belief nets for natural language call-routing. Paper presented at the 2011 IEEE international conference on acoustics, speech and signal processing.
Sun, D., Shi, H., & Chang, L. (2013). Application of support vector regression in prediction of application of support vector regression in prediction of due date under uncertain assemble-to-order environment. Journal of Computer Applications, 8, 2362–2365. CrossRef
Toshev, A., & Christian, S. (2014). DeepPose: Human pose estimation via deep neural networks. Paper presented at the 2014 IEEE conference on computer vision and pattern recognition (CVPR).
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.
Weng, Z. K. (1996). Manufacturing lead times, system utilization rates and lead-time-related demand. European Journal of Operational Research, 89(2), 259–268. CrossRef
Yang, S., Lee, H., & Guo, J. (2013). Multiple common due dates assignment and scheduling problems with resource allocation and general position-dependent deterioration effect. The International Journal of Advanced Manufacturing Technology, 67(1–4), 181–188. doi: 10.1007/s00170-013-4763-x. CrossRef
Zhong, R. Y., Huang, G. Q., Dai, Q., & Zhang, T. (2013). Estimation of lead time in the RFID-enabled real-time shopfloor production with a data mining model. Paper presented at the The 19th international conference on industrial engineering and engineering management.
Zhu, H., Liu, F., Liu, Q., & Shao, X. U. (2009). A predictive method for order due date based on real-time state of workshop. China Mechanical Engineering, 3, 300–304.
Ziarnetzky, T., & Mönch, L. (2016). Incorporating engineering process improvement activities into production planning formulations using a large-scale wafer fab model. International Journal of Production Research, 54(21), 6416–6435. CrossRef
- Deep neural networks based order completion time prediction by using real-time job shop RFID data
- Springer US
- Journal of Intelligent Manufacturing
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
in-adhesives, MKVS, Neuer Inhalt/© Zühlke, Technisches Interface Design/© scyther5 | Getty Images | iStock