• N. Celik and Y. Son, 2011, Sequential Monte Carlo-based Fidelity Selection in Dynamic-Data-Driven Adaptive Multi-Scale Simulations, International Journal of Production Research, accepted.
  • N. Celik and Y. Son, 2011, State Estimation of a Shop Floor using Improved Resampling Rules for Particle Filtering, International Journal of Production Economics,134, 224-237.
  • N. Celik, S. Lee, K. Vasudevan, and Y. Son, 2010, DDDAS-based Multi-Fidelity Simulation Framework for Supply Chain Systems, IIE Transactions on Operations Engineering, 42, 325-341. [This paper has been featured in the IE Magazine that is sent to all IIE members.]
  • Doucet, A., Godsill, S.,  Andrieu, C., 2000. On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, 10, 197–208.
  • Gordon, N., Salmond, D., Smith, A. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F-Radar and Signal Processing, 140(2), 107–113.
  • De Freitas, J.F.G., Niranjan M., Gee A.H. and Doucet A. (2000). Sequential Monte Carlo Methods to Train Neural Network Models, Neural Computation, 12(4), 955-993.
  • Fu, M.C., 2002. Optimization for simulation: theory VS practice. INFORMS Journal on Computing, 14 (3), 192–215.
  • Zhou, E., Fu, M., Marcus, S. I., 2008. A particle filtering for randomized optimization algorithms, In Proceedings of the Winter Simulation Conference 2008, Miami, FL, USA.

IERC Papers

  • X. (Eileen) Shi and N. Celik, A Minimum Relative Entropy-based Density Selection Scheme for Bayesian Estimations of Energy-related Problems, In Proceedings of the Annual Industrial and Systems Engineering Research Conference 2012, Orlando, Florida, May 19-23, 2012.
  • N. Celik, E. Mazhari, and Y. Son, Automatic Partitioning of Large Scale Simulation in Grid Computing for Run Time Reduction, In Proceedings of the Annual Industrial Engineering Research Conference 2009, Miami, FL, USA, May 29-June 3, 2009.