State estimation of a shop floor using improved resampling rules for particle filtering

International Journal of Production Economics

Volumn 134, Issue 1, 2011, Pages 224-237

  Celik, Nurcin ^a   Son, Young Jun ^b  

^a UNIVERSITY OF MIAMI   (United States)

^b University of Arizona   (United States)

Author keywords

Importance sampling; Resampling rules; Sequential Monte Carlo methods; Shop floor state estimation; Simulation

Indexed keywords

IMPORTANCE SAMPLING; RESAMPLING; SEQUENTIAL MONTE CARLO METHODS; SHOP FLOOR STATE ESTIMATION; SIMULATION;

ESTIMATION; FLOORS; INDUSTRIAL APPLICATIONS; MONTE CARLO METHODS; NONLINEAR FILTERING; SUPPLY CHAINS;

STATE ESTIMATION;

EID: 80052355327     PISSN: 09255273     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijpe.2011.07.003     Document Type: Article

References (62)

1. F.J. Anscombe Large sample theory of sequential estimation Proceedings of the Cambridge Philosophical Society 48 1952 600 607
2. Arnaud, E., Le Gland, F., 2009. SMC with adaptive resampling: large sample asymptotics. In: Proceedings of the 2009 IEEE Workshop on Statistical Signal Processing, Cardiff, AugustSeptember, pp. 481484.
3. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp A tutorial on particle filters for online nonlinear/non-gaussian Bayesian tracking IEEE Transactions on Signal Processing 50 2 2002 174 188 (Pubitemid 34291500)
4. Azzabou, N., Paragios, N., Guichard, F., 2005. Application of Particle Filtering to Image Enhancement, Research Report submitted to CERTIS, ENPC, France.
5. Bayraksan, G., 2005. Monte Carlo Sampling-based Methods in Stochastic Programming, Dissertation, Submitted to the Faculty of the Graduate School of The University of Texas at Austin.
6. C. Berzuini, N.G. Best, W. Gilks, and C. Larizza Dynamic conditional independent models and Markov chain Monte Carlo methods Journal of American Statistical Association 92 1997 1403 1412
7. P. Brasnett, L. Mihaylova, D. Bull, and N. Canagarajah Sequential Monte Carlo tracking by fusing multiple cues in video sequences Image and Vision Computing, Elsevier Science 28 1 2007 1217 1227 (Pubitemid 46764422)
8. Burke, D., Ghosh A., Heidrich, W., 2005. Bidirectional importance sampling for direct illumination. In: Bala, K., Dutré, P. (Eds.), Eurographics Symposium on Rendering.
9. F. Cadini, D. Avram, and E. Zio A Monte Carlo-based technique for estimating the operation modes of hybrid dynamic systems Reliability: Theory & Applications 17 1 2010 106 114
10. J. Carpenter, P. Clifford, and P. Fearnhead An improved particle filter for non-linear problems Proceedings of IEE- Radar, Sonar, and Navigation 146 2004 2 7
11. Casarin, R., Sartore, D., 2008. Matrix-state particle filter for Wishart stochastic volatility processes. Discussion Paper (No: 0816), Dipartimento di Scienze Economiche, Università degli Studi di Brescia, Italy.
12. Celik, N., Son, Y. Sequential Monte Carlo-based fidelity selection in dynamic-data-driven adaptive multi-scale simulations. International Journal of Production Research, in press.
13. Celik, N., Son, Y., 2010. State estimation of a supply chain using improved resampling rules for particle filtering. In: Proceedings of the Winter Simulation Conference 2010, Baltimore, MD, USA, 19982010.
14. Chen, Y., 2001. Sequential Importance Sampling with Resampling: Theory and Applications, Dissertation, Submitted to the Faculty of the Graduate School of Stanford University.
15. J. Cheng, and M.J. Druzdzel AIS-BN: an adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks Journal of Artificial Intelligence Research 13 2000 155 188 (Pubitemid 33682085)
16. R. Douc, and É. Moulines Limit theorems for weighted samples with applications to sequential Monte Carlo methods Annals of Statistics 36 2008 2344 2376
17. Douc, R., Cappe, O., 2005. Comparison of resampling schemes for particle filtering. In: Proceedings of the Fourth International Symposium on in Image and Signal Processing and Analysis, Zagreb, Croatia, pp. 6469. (Pubitemid 44459664)
18. Doucet, A., 1998. On Sequential Simulation-based Methods for Bayesian Filtering. Technical Report, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
19. A. Doucet, J.F.G. de Freitas, and N.J. Gordon An introduction to sequential Monte Carlo methods A. Doucet, J.F.G. de Freitas, N.J. Gordon, Sequential Monte Carlo methods in practice 2001 Springer-Verlag New York, USA
20. A. Doucet, N. de Freitas, and N. Gordon Sequential Monte Carlo Methods in Practice 2001 Springer New York, USA
21. A. Doucet, S. Godsill, and C. Andrieu On sequential Monte Carlo sampling methods for Bayesian filtering Statistics and Computing 10 2000 197 208
22. M. Dowd A sequential Monte Carlo approach for marine ecological prediction Environmetrics 17 2006 435 455 (Pubitemid 44065244)
23. H.C. Dreyer, E. Alfnes, J.O. Strandhagen, and M.K. Thomassen Global supply chain control systems: a conceptual framework for the global control centre Production Planning and Control 20 2 2009 147 157
24. Fearnhead, P., 1998. Sequential Monte Carlo methods in filter theory, Dissertation Submitted to the Faculty of the Graduate School of University of Oxford.
25. P. Fearnhead, and P. Clifford On-line inference for hidden Markov models via particle filters Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65 2003 887 899 (Pubitemid 37434236)
26. Flury, T., Shephard, N., 2008. Bayesian Inference Based only on Simulated Likelihood: Particle Filter Analysis of Dynamic Economic Models. Technical Report (No: 413), Department of Economics, University of Oxford, UK.
27. S.J. Godsill, and P.J.W. Rayner Robust reconstruction and analysis of autoregressive signals in impulsive noise using the Gibbs sampler IEEE Transactions on Speech and Audio Processing 6 4 1998 352 372 (Pubitemid 128720668)
28. L.A. Goodman On the exact variance of products Journal of the American Statistical Association 55 292 1960 708 713
29. M.M. Guterman, and Z.H. Nitecki Differential Equations-a First Course 2006 Indo American Books Delhi
30. Harvard Business Review Supply Chain Management 2006 Harvard Business Press Boston
31. Haug, A.J., 2005. A Tutorial on Bayesian Estimation and tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes. Mitre Technical Report. MTR 05W0000004.
32. Kim, K.J., Iltis, R.A. 2005. Proceedings of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers, Monterey, California, pp. 829833.
33. G. Kitagawa Monte Carlo filter and smoother for non-Gaussian nonlinear state space models Journal of Computational and Graphical Statistics 5 1996 1 25
34. Kong, A., 1992. A Note on Importance Sampling Using Standardized Weights, Technical Report No. 348, Department of Statistics, University of Chicago, Chicago, Illinois.
35. A. Kong, J.S. Liu, and W.H. Wong Sequential imputations and Bayesian missing data problems Journal of American Statistical Association 89 1994 278 288
36. J. Koop On upper limits to the difference in bias between two ratio estimates Metrika 5 1 1962 145 149
37. J.C. Koop A note on the bias of the ratio estimate Bulletin of the International Statistical Institute 21 2 1951 141 146
38. KPMG Report, 2009. The road to recovery in the global semiconductor industry.
39. Lee, H.L., 2004. The triple-A supply chain, Harvard Business Review, Onpoint Article.
40. C.M. Lee A Bayesian approach to determine the value of information in the newsboy problem International Journal of Production Economics 112 2008 2008 391 402
41. J.S. Liu, R. Chen, and T. Logvinenko A theoretical framework for sequential importance sampling with resampling A. Doucet, J.F.G. de Freitas, N.J. Gordon, Sequential Monte Carlo Methods in Practice 2001 Springer-Verlag Berlin, Germany
42. J.S. Liu, and R. Chen Sequential Monte Carlo methods for dynamic systems Journal of the American Statistical Association 93 443 1998 1032 1044
43. X.L. Meng On the absolute bias ratio of ratio estimators Statistics and Probability Letters 18 1993 345 348
44. Muhlich, M., 2003. Particle filters an overview. JWGoethe-Universitat Frankfurt, Filter-Workshop, Bucuresti.
45. J. Mula, R. Poler, J.P. García-Sabater, and F.C. Lario Models for production planning under uncertainty: a review International Journal of Production Economics 103 1 2006 271 285 (Pubitemid 43866480)
46. Patterson, D., 2008. The Delta Method and Applications, Technical Report.
47. M. Quirk, and J. Serda Semiconductor Manufacturing Technology 2001 Prentice Hall
48. Randall, D.A., 2006. Taylor Series. Technical Report.
49. G. Ridgeway, and D. Madigan A sequential Monte Carlo method for Bayesian analysis of massive datasets Data Mining and Knowledge Discovery 7 3 2003 301 319
50. B. Ristic, S. Arulampalam, and N. Gordon Beyond the Kalman Filter: Particle Filters for Tracking Applications 2004 Artech House London, United Kingdom
51. T.M. Rupp, and M. Ristic Fine planning for supply chains in semiconductor manufacture Journal of Materials Processing Technology 107 13 2000 390 397 (Pubitemid 32072630)
52. D. Schulz, and W. Burgard Probabilistic state estimation of dynamic objects with a moving mobile robot Robotics and Autonomous Systems 34 2001 2 3
53. A.F.M. Smith, and A.E. Gelfand Bayesian statistics without tears: a sampling-resampling perspective The American Statistician 46 1992 84 88
54. K. Surekha, and G. Ghali The speed of adjustment and production smoothing: Bayesian estimation International Journal of Production Economics 71 2001 55 65 (Pubitemid 32478607)
55. S. Thrun, J. Langford, V. Verma, and I. Verma Risk Sensitive Particle Filters, Advances in Neural Information Processing Systems 2002 MIT Press Cambridge, MA
56. M. Tin Comparison of some ratio estimators Journal of the American Statistical Association 60 309 1965 294 307
57. van der Merwe, R., Doucet, A., de Freitas, N., Wan, E., 2000. The unscented particle filter. Advances in Neural Information Processing Systems, December 2000.
58. E. Wan, and R. van der Merwe The unscented Kalman filter S. Haykin, Kalman Filtering and Neural Networks 2002 Wiley Publishing
59. Q. Wen, and P. Qicong An improved particle filter algorithm based on neural network for target tracking Z. Shi, K. Shimohara, D. Feng, International Federation for Information Processing 228 2007 Springer-Verlag Boston 297 305
60. Xia, H., Ding, Y., Mallick, B. Bayesian hierarchical model for integrating multi-resolution metrology data. Technometrics, submitted.
61. X. Xu, and B. Li Adaptive Rao-Blackwellised particle filter and its evaluation for tracking in surveillance IEEE Transactions on Image Processing 16 3 2007 838 849 (Pubitemid 46431978)
62. Y. Zejian, Z. Nanning, and J. Xinchun The Gauss-Hermite particle filter Dian Zi Xue Bao 31 7 2003 970 973