Future directions in response surface methodology for simulation

ABSTRACT

As a tool for gradient estimation and sensitivity analysis in discrete simulation, response surface methodology possesses noteworthy advantages in comparison to some of the more recently developed techniques. This paper surveys future directions for research, development, and application of response surface methodology in discrete simulation.

REFERENCES

• 1: Kenneth W. Bauer, Jr. , Sekhar Venkatraman , James R. Wilson, Estimation procedures based on control variates with known covariance matrix, Proceedings of the 19th conference on Winter simulation, p.334-341, December 14-16, 1987, Atlanta, Georgia, United States [doi>10.1145/318371.318599]
• 2: Box, G. E. P. and Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society B13, 1-38.
• 3: Cogliano, V. J. (1982). Sensitivity analysis and model identification in simulation studies. Unpublished Ph.D. Dissertation, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York.
• 4: Cooley, B. J. and Houck, E. C. (1{}82). A variancereduction strategy for RSM simulation studies. Decision Sciertce8 13, 303-321.
• 5: Cooley, B. J. and Houck, E. C. (1983). On an alternative variance-reduction strategy for RSM s~mulation studies. Decis/on Science8 14, 134-137.
• 6: Peter W. Glynn, Stochastic approximation for Monte Carlo optimization, Proceedings of the 18th conference on Winter simulation, p.356-365, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318459]
• 7: Heidelberger, P. (1987). Limitations of infinitesimal perturbation analysis. Technical Report RC 11891, IBM Thomas J. Watson Research Center, Yorktown Heights, New York.
• 8: Ho, Y. C., Eyler, ~/L A., and Chien, T. T. (1979). A gradient technique for for general buffer storage design in a serial production line. International Journal of Production Research 17, 557-580.
• 9: Ho, Y. C., Surl, R., Cao, X. R., Cassandras, C., and Zazanis, M. A. (1987). Perturbation analysis {PA) of discrete event systems--limited or unlimited? Spring Joint National Meeting of The Institute of Management Sciences and the Operations Research Society of America, New Orleans, Louisiana.
• 10: Johnston, J. (1972). Econometric Methods, Second Edition. McGraw-Hiil, New York.
• 11: Nozari, A., Arnold, S. F., and Pegden~ C.D. {1984). Control variates for multipopulation simulation experiments. IIE Transactions 16, 159-169.
• 12: A. Nozari , S. F. Arnold , C. D. Pegden, Statistical analysis for use with the Schruben and Margolin correlation induction strategy, Operations Research, v.35 n.1, p.127-139, Jan.-Feb. 1987 [doi>10.1051/ro:2001107]
• 13: Aćacio M. de O. Porta Nova , James R. Wilson, Using control variates to estimate multiresponse simulation metamodels, Proceedings of the 18th conference on Winter simulation, p.326-334, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318454]
• 14: Martin I. Reiman , Alan Weiss, Sensitivity analysis via likelihood ratios, Proceedings of the 18th conference on Winter simulation, p.285-289, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318450]
• 15: Lee Schruben, Simulation optimization using frequency domain methods, Proceedings of the 18th conference on Winter simulation, p.366-369, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318460]
• 16: Lee W. Schruben , V. James Cogliano, An experimental procedure for simulation response surface model identification, Communications of the ACM, v.30 n.8, p.716-730, Aug. 1987 [doi>10.1145/27651.27656]
• 17: Schruben, L. W. and M:argolln, B. H. (1978). Pseudorandom number assignment in statistically designed simulation and distribution sampling experiments. Journal of the American Statistical Association 73, 504-525.
• 18: Suri, R. and Zazanis, M. A. (1987). Perturbation analysis gives strongly consistent sensitivity estimates for the M/G/1 queue. Technical Report, Division of Applied Sciences, Harvard University, Boston, Massachusetts.
• 19: Jeffrey D. Tew , James R. Wilson, Validation of correlation-induction strategies for simulation experiments, Proceedings of the 17th conference on Winter simulation, p.190-195, December 11-13, 1985, San Francisco, California, United States [doi>10.1145/21850.253148]
• 20: Tew, J. D. and Wilson, J. R. (1986). Validation of correlation-induction strategies for simulation experiments. Research Memorandum 86-12, School of Industrial Engineering, Purdue University, West Lafayette, Indiana.
• 21: Jeffrey D. Tew , James R. Wilson, Metamodel estimation using integrated correlation methods, Proceedings of the 19th conference on Winter simulation, p.409-418, December 14-16, 1987, Atlanta, Georgia, United States [doi>10.1145/318371.318621]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING

Additional Classification:
  G. Mathematics of Computing

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.8 Types of Simulation
          Subjects: Discrete event

General Terms:
Design, Performance, Theory