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Simulation sensitivity analysis: A frequency domain approach

Authors:

Lee W. Schruben,

Vincent James CoglianoAuthors Info & Claims

WSC '81: Proceedings of the 13th conference on Winter simulation - Volume 2

Pages 455 - 459

Published: 09 December 1981 Publication History

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Abstract

A procedure is presented for assessing the sensitivity of a discrete event digital simulation model to the values assumed for its input parameters. A frequency domain approach is taken where the input parameters oscillate throughout a run of the model. Parameter sensitivities are indicated by changes in the frequency spectrum of the simulation response. The spectrum can be used to identify a regression model for the simulated response surface. Several continuous parameters may be screened in a single run.

References

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Chatfield, C. (1975), The Analysis of Time Series: Theory and Practice, Chapman and Hall, London, 263 p.

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[2]

Hillier, F.S. and G.J. Lieberman (1980), Introduction to Operations Research, Third Edition, Holden-Day, San Francisco, 829 p.

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[3]

Kleijnen, J.P.C. (1975), Statistical Techniques in Simulation, Part II, Marcel Dekker, New York, 775 p.

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Barton RNakayama MSchruben L(2017)History of improving statistical efficiencyProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242193(1-23)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242193
Fu MHenderson S(2017)History of seeking better solutions, aka simulation optimizationProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242192(1-27)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242192
Wu XBarton RHuschka TChick S(2016)Fourier trajectory analysis for identifying system congestionProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042158(401-412)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042158
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Index Terms

Simulation sensitivity analysis: A frequency domain approach
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
  2. Modeling and simulation
    1. Simulation types and techniques
      1. Discrete-event simulation
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis

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WSC '81: Proceedings of the 13th conference on Winter simulation - Volume 2

December 1981

362 pages

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IEEE Press

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Published: 09 December 1981

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Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Barton RNakayama MSchruben L(2017)History of improving statistical efficiencyProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242193(1-23)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242193
Fu MHenderson S(2017)History of seeking better solutions, aka simulation optimizationProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242192(1-27)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242192
Wu XBarton RHuschka TChick S(2016)Fourier trajectory analysis for identifying system congestionProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042158(401-412)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042158
Pappert FUhlig TRose OHill RKuhl M(2013)Using a frequency domain approach on model comparisonProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2675955(4016-4017)Online publication date: 8-Dec-2013
https://dl.acm.org/doi/10.5555/2675983.2675955
Wieland JSchmeiser BNicol DFujimoto R(2006)Stochastic gradient estimation using a single design pointProceedings of the 38th conference on Winter simulation10.5555/1218112.1218188(390-397)Online publication date: 3-Dec-2006
https://dl.acm.org/doi/10.5555/1218112.1218188
Azadivar F(1999)Simulation optimization methodologiesProceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 110.1145/324138.324168(93-100)Online publication date: 1-Dec-1999
https://dl.acm.org/doi/10.1145/324138.324168
Gaury EKleijnen J(1998)Risk analysis of robust system designProceedings of the 30th conference on Winter simulation10.5555/293172.293529(1533-1540)Online publication date: 1-Dec-1998
https://dl.acm.org/doi/10.5555/293172.293529
Hazra MPark SSadowski DSeila A(1994)Characterizing a nonstationary M/G/1 queue using bode plotsProceedings of the 26th conference on Winter simulation10.5555/193201.194084(377-382)Online publication date: 11-Dec-1994
https://dl.acm.org/doi/10.5555/193201.194084
Azadivar FCrain R(1992)A tutorial on simulation optimizationProceedings of the 24th conference on Winter simulation10.1145/167293.167332(198-204)Online publication date: 1-Dec-1992
https://dl.acm.org/doi/10.1145/167293.167332
Mitra MPark SNelson BKelton WClark G(1991)Solution to the indexing problem of frequency domain simulation experimentsProceedings of the 23rd conference on Winter simulation10.5555/304238.304368(907-915)Online publication date: 1-Dec-1991
https://dl.acm.org/doi/10.5555/304238.304368
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Parameter sensitivity analysis of a 5-DoF parallel manipulator

Risk analysis and sensitivity analysis: antithesis or synthesis?

Evolutionary algorithm parameter tuning with sensitivity analysis

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Recommendations

Parameter sensitivity analysis of a 5-DoF parallel manipulator

Risk analysis and sensitivity analysis: antithesis or synthesis?

Evolutionary algorithm parameter tuning with sensitivity analysis

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