565
Views
13
CrossRef citations to date
0
Altmetric
Articles

Combining simulation experiments and analytical models with area-based accuracy for performance evaluation of manufacturing systems

, ORCID Icon &
Pages 266-283 | Received 13 Nov 2017, Accepted 31 May 2018, Published online: 22 Feb 2019

References

  • Askin, R.G. and Standridge, C.R. (1993) Modeling and Analysis of Manufacturing Systems, Wiley New York, NY.
  • Bakr, M.H., Bandler, J.W., Madsen, K., Rayas-Sánchez, J.E. and Sondergaard, J. (2000) Space-mapping optimization of microwave circuits exploiting surrogate models. IEEE Transactions on Microwave Theory and Techniques, 48(12), 2297–2306.
  • Bandler, J.W., Biernacki, R.M., Chen, S.H., Grobelny, P.A. and Hemmers, R.H. (1994) Space mapping technique for electromagnetic optimization. IEEE Transactions on Microwave Theory and Techniques, 42(12), 2536–2544.
  • Bandler, J.W., Biernacki, R.M., Chen, S.H., Hemmers, R.H. and Madsen, K. (1995) Electromagnetic optimization exploiting aggressive space mapping. IEEE Transactions on Microwave Theory and Techniques, 43(12), 2874–2882.
  • Bandler, J.W., Georgieva, N., Ismail, M.A., Rayas-Sánchez, J.E. and Zhang, Q.-J. (2001) A generalized space-mapping tableau approach to device modeling. IEEE Transactions on Microwave Theory and Techniques, 49(1), 67–79.
  • Buzacott, J.A. and Shanthikumar, J.G. (1993) Stochastic Models of Manufacturing Systems, Prentice Hall.
  • Chang, K.J., Haftka, R.T., Giles, G.L. and Kao, I.-J. (1993) Sensitivity-based scaling for approximating structural response. Journal of Aircraft, 30(2), 283–288.
  • Chen, R., Xu, J., Zhang, S., Chen, C.H. and Lee, L.H. (2015) An effective learning procedure for multi-fidelity simulation optimization with ordinal transformation, in Proceeding of the 2015 IEEE International Conference on Automation Science and Engineering, IEEE Press, Piscataway, NJ, pp. 702–707.
  • Cressie, N. (1992) Statistics for spatial data. Terra Nova, 4(5), 613–617.
  • Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A.J. and Vapnik, V. (1996) Support vector regression machines, in Advances in Neural Information Processing Systems 9, pp. 155–161.
  • Gano, S.E., Renaud, J.E. and Sanders, B. (2005) Hybrid variable fidelity optimization by using a kriging-based scaling function. AIAA Journal, 43(11), 2422–2433.
  • Gershwin, S.B. (1994) Manufacturing Systems Engineering, Prentice Hall.
  • Haftka, R.T. (1991) Combining global and local approximations. AIAA Journal, 29(9), 1523–1525.
  • Han, Z., Zimmerman, R. and Görtz, S. (2012) Alternative cokriging method for variable-fidelity surrogate modeling. AIAA Journal, 50(5), 1205–1210.
  • Han, Z.H. and Görtz, S. (2012) Hierarchical kriging model for variable-fidelity surrogate modeling. AIAA Journal, 50(9), 1885–1896.
  • Han, Z.H., Görtz, S. and Zimmermann, R. (2013) Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function. Aerospace Science and Technology, 25(1), 177–189.
  • Haykin, S.S. (2009) Neural Networks and Learning Machines, Pearson Prentice Hall.
  • Helton, J.C. and Davis, F.J. (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety, 81(1), 23–69.
  • Hu, J., Zhou, Q., Jiang, P. and Xie, T. (2016) An improved hierarchical kriging for variable-fidelity surrogate modeling, in Proceedings of the 2016 International Conference on Cybernetics, Robotics and Control, IEEE Press, Piscataway, NJ, pp. 86–90.
  • Huang, D., Allen, T.T., Notz, W.I. and Miller, R.A. (2006) Sequential kriging optimization using multiple-fidelity evaluations. Structural and Multidisciplinary Optimization, 32(5), 369–382.
  • Kennedy, M.C. and O’Hagan, A. (2000) Predicting the output from a complex computer code when fast approximations are available. Biometrika, 87(1), 1–13.
  • Leary, S.J., Bhaskar, A. and Keane, A.J. (2003) A knowledge-based approach to response surface modelling in multifidelity optimization. Journal of Global Optimization, 26(3), 297–319.
  • Lewis, R. and Nash, S. (2000) A multigrid approach to the optimization of systems governed by differential equations, in Proceedings of the 8th Symposium on Multidisciplinary Analysis and Optimization, page 4890.
  • Li, J. and Meerkov, S.M. (2009) Production Systems Engineering, Springer.
  • Lin, Z., Matta, A., Li, N. and Shanthikumar, J.G. (2016) Extended kernel regression: A multi-resolution method to combine simulation experiments with analytical methods, in Proceedings of the 2016 Winter Simulation Conference, IEEE Press, Piscataway, NJ, pp. 590–601.
  • Lophaven, S.N., Nielsen, H.B. and Søndergaard, J. (2002) Aspects of the Matlab toolbox dace. Technical report, Informatics and Mathematical Modelling, Technical University of Denmark, DTU.
  • McKay, M.D., Beckman, R.J. and Conover, W.J. (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239–245.
  • Nadaraya, E.A. (1964) On estimating regression. Theory of Probability & Its Applications, 9(1), 141–142.
  • Osorio, C. and Bierlaire, M. (2013) A simulation-based optimization framework for urban transportation problems. Operations Research, 61(6), 1333–1345.
  • Papadopoulos, C.T., O’Kelly, M.E., Vidalis, M.J. and Spinellis, D. (2009) Analysis and Design of Discrete Part Production Lines, Springer.
  • Robinson, T.D., Eldred, M.S., Willcox, K.E. and Haimes, R. (2008) Surrogate-based optimization using multifidelity models with variable parameterization and corrected space mapping. AIAA Journal, 46(11), 2814–2822.
  • Sacks, J., Welch, W.J., Mitchell, T.J. and Wynn, H.P. (1989) Design and analysis of computer experiments. Statistical Science, 4(4), 409–423.
  • Sun, G., Li, G., Stone, M. and Li, Q. (2010) A two-stage multi-fidelity optimization procedure for honeycomb-type cellular materials. Computational Materials Science, 49(3), 500–511.
  • Sun, G., Li, G., Zhou, S., Xu, W., Yang, X. and Li, Q. (2011) Multi-fidelity optimization for sheet metal forming process. Structural and Multidisciplinary Optimization, 44(1), 111–124.
  • Suri, R. and Leung, Y.T. (1987) Single run optimization of a siman model for closed loop flexible assembly systems, in Proceedings of the 1987 Winter Simulation Conference, Association for Computing Machinery, Atlanta, GA, pp. 738–748.
  • Tempelmeier, H. and Kuhn, H. (1993) Flexible Manufacturing Systems: Decision Support for Design and Operation, John Wiley & Sons.
  • Wahba, G. (1990) Spline Models for Observational Data, SIAM, Philadelphia, PA.
  • Wand, M.P. and Jones, M.C. (1995) Kernel Smoothing, Chapman and Hall/CRC.
  • Wang, F. and Zhang, Q.-J. (1997) Knowledge-based neural models for microwave design. IEEE Transactions on Microwave Theory and Techniques, 45(12), 2333–2343.
  • Wasserman, L. (2006) All of Nonparametric Statistics, Springer.
  • Watson, G.S. (1964) Smooth regression analysis. Sankhyā: The Indian Journal of Statistics, Series A, 359–372.
  • Watson, P.M. and Gupta, K.C. (1996) EM-ANN models for microstrip vias and interconnects in dataset circuits. IEEE Transactions on Microwave Theory and Techniques, 44(12), 2495–2503.
  • Xu, J., Zhang, S., Huang, E., Chen, C.H., Lee, L.H. and Celik, N. (2016) Mo2tos: Multi-fidelity optimization with ordinal transformation and optimal sampling. Asia-Pacific Journal of Operational Research, 33(3), 1650017.
  • Yamazaki, W. and Mavriplis, D.J. (2013) Derivative-enhanced variable fidelity surrogate modeling for aerodynamic functions. AIAA Journal, 51(1), 126–137.
  • Zhou, Q., Shao, X., Jiang, P., Zhou, H. and Shu, L. (2015) An adaptive global variable fidelity metamodeling strategy using a support vector regression based scaling function. Simulation Modelling Practice and Theory, 59, 18–35.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.