Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops

Journal of Intelligent Manufacturing

Volumn 29, Issue 7, 2018, Pages 1485-1500

  Wang, Chuang ^a   Jiang, Pingyu ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Manifold learning; Recessive disturbances; Rescheduling decision mechanism; RFID; SLLE

Indexed keywords

ENGINEERING EDUCATION; MACHINING; MANAGERS; MANUFACTURE; NEURAL NETWORKS; PRODUCTION CONTROL; RADIO FREQUENCY IDENTIFICATION (RFID); SUPPORT VECTOR MACHINES;

DECISION MECHANISM; GENERAL REGRESSION NEURAL NETWORK; LEAST SQUARE SUPPORT VECTOR MACHINES; MANIFOLD LEARNING; NUMERICAL EXPERIMENTS; RECESSIVE DISTURBANCES; SLLE; SUPERVISED LOCALLY LINEAR EMBEDDING;

JOB SHOP SCHEDULING;

EID: 84954314072     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-016-1194-1     Document Type: Article

References (39)

1. Abumaizar, R. J., & Svestka, J. A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research, 35(7), 2065–2082
2. Ahmed, E., & Lee, C. S. (2004). Inferring 3D body pose from silhouettes using activity manifold learning. In Paper Presented at the 2013 IEEE Conference on Computer Vision and Pattern Recognition
3. Akturk, M. S., & Gorgulu, E. (1999). Match-up scheduling under a machine breakdown. European Journal of Operational Research, 112(1), 81–97
4. Brewer, A., Sloan, N., & Landers, T. L. (1999). Intelligent tracking in manufacturing. Journal of Intelligent Manufacturing, 10(3), 245–250
5. Cristianini, N., & John, S. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press
6. Dong, Y., & Jang, J. (2011). Production rescheduling for machine breakdown at a job shop. International Journal of Production Research, 50(5), 2681–2691
7. Editor (Ed.). (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry. In Final Report of the Industrie 4.0 Working Group, Forschungsunion
8. Fattahi, P., & Fallahi, A. (2010). Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability. CIRP Journal of Manufacturing Science and Technology, 2(2), 114–123
9. Herroelen, W., & Roel, L. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289–306
10. 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. doi:10.1016/j.rcim.2006.05.006
11. Jiang, P., & Cao, W. (2013). An RFID-driven graphical formalized deduction for describing the time-sensitive state and position changes of work-in-progress material flows in a job-shop floor. Journal of Manufacturing Science and Engineering, 135(3), 189–197
12. Jiang, Q., Jia, M., Hu, J., & Feiyun, X. (2009). Machinery fault diagnosis using supervised manifold learning. Mechanical Systems and Signal Processing, 23(1), 2301–2311
13. Katragjini, K., Vallada, E., & Ruiz, R. (2013). Flow shop rescheduling under different types of disruption. International Journal of Production Research, 51(3), 780–797
14. Kouropteva, O., Okun, O., Ainen, M. P. (2003). Supervised Locally Linear Embedding Algorithm for Pattern Recognition. Paper presented at the IbPRIA,. (2003). Puerto de Andratx. Spain: Mallorca
15. Li, B., & Zhang, Y. (2011). Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis. Mechanical Systemsand Signal Processing, 25(8), 3125–3134
16. Li, R. K. (1995). A new rescheduling method for computer based scheduling systems. International Journal of Production Research, 33(8), 2097–2110
17. Lin, F., Yeh, C., & Lee, M. (2011). The use of hybrid manifold learning and support vector machines in the prediction of business failure. Knowledge-Based Systems, 24(1), 95–101
18. Liu, M., Shan, H., Jiang, Z., Ge, M., Hu, J., & Zhang, M. (2009). Dynamic rescheduling optimization of job-shop under uncertain conditions. Journal of Mechanical Engineering, 45(10), 137–142
19. Liu, M., Zhang, X., Zhang, M., Ge, M., & Hu, J. (2014). Rescheduling decision method of manufacturing shop based on profit-loss cloud model. Control and Decision, 29(8), 1458–1464
20. Lv, S., & Qiao, L. (2014). Process planning and scheduling integration with optimal rescheduling strategies. International Journal of Computer Integrated Manufacturing, 27(7), 638–655
21. Malte, B., Niklas, F., Michael, K., & Marius, R. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. International Journal of Science. Engineering and Technology, 8(1), 37–44
22. Martin, H. C. L., & Anil, K. J. (2006). Incremental nonlinear dimensionality reduction by manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(3), 377–391
23. Mehrjerdi, Y. Z. (2008). RFID-enabled systems: A brief review. Assembly Automation, 28(3), 235–245
24. Mohsen, S., Razieh, S., & Ziari, M. B. (2010). QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components. European Journal of Medicinal Chemistry, 45(10), 4499–4508
25. Neal, P., & Alfred, O. H. I. (2004). Manifold learning algorithms for localization in wireless sensor networks. In Paper presented at the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
26. Olumolade, M. O. (1996). Reactive scheduling system for cellular manufacturing with failure-prone machines. International Journal of Computer Integrated Manufacturing, 9(2), 131–144. doi:10.1080/095119296131742
27. Ovacik, I. M., & Uzsoy, R. (1997). Decomposition methods for complex factory scheduling problems. Berlin: Springer
28. Pratap, S., Daultani, Y., Tiwari, M. K., & Mahanty, B. (2015). Rule based optimization for a bulk material handling port operation. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1108-7
29. Qiao, F., Wu, Q., Li, L., Wang, Z., & Shi, B. (2011). A fuzzy Petri net-based reasoning method for rescheduling. Transactions of the Institute of Measurement and Control, 33(3), 435–455. doi:10.1177/0142331208100100
30. Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(22), 2323–2326
31. Sabuncuoglu, & Bayız, M. (2000). Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research, 126(3), 567–586
32. Saul, L. K., & Roweis, S. (2003). Think globally, fit locally: Unsupervised learning of low dimensional manifolds. The Journal of Machine Learning Research, 4(12), 119–155
33. Suwa, H. (2007). A new when-to-schedule policy in online scheduling based on cumulative task delays. International Journal of Production Economics, 110(1), 175–186. doi:10.1016/j.ijpe.2007.02.015
34. Tenenbaum, J. B., de Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(22), 2319–2323
35. Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: A framework of strategies, policies, and methods. Journal of Scheduling, 6(1), 39–62
36. Vieira, G. E., Herrmann, W. J., & Lin, E. (2000). Predicting the performance of rescheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256–266
37. Wang, S., Xi, L., & Zhou, B. (2008). FBS-enhanced agent-based dynamic scheduling in FMS. Engineering Applications of Artificial Intelligence, 21(4), 644–657
38. 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. doi:10.1080/0951192X.2012.749532
39. Zhou, G., Zheng, M., & Xiao, Z. (2011). Dynamic job rescheduling using RFID technology. Internet Manufacturing and Services, 3(1), 42–58