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Reducing and Calibrating for Input Model Bias in Computer Simulation

Author

Listed:
  • Lucy E. Morgan

    (Department of Management Science, Lancaster University, Lancaster LA1 4YR, United Kingdom)

  • Luke Rhodes-Leader

    (Statistics and Operational Research Centre for Doctoral Training in Partnership with Industry (STOR-i), Lancaster University, Lancaster LA1 4YR, United Kingdom)

  • Russell R. Barton

    (Department of Supply Chain and Information Systems, The Pennsylvania State University, University Park, Pennsylvania 16802)

Abstract

Input model bias is the bias found in the output performance measures of a simulation model caused by estimating the input distributions/processes used to drive it. When the simulation response is a nonlinear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is amongst the errors that arise. In this paper, we introduce a method that recalibrates the input parameters of parametric input models to reduce the bias in the simulation output. The proposed method is based on sequential quadratic programming with a closed form analytical solution at each step. An algorithm with guidance on how to practically implement the method is presented. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling error. Summary of Contribution: This paper furthers the understanding and treatment of input modelling error in computer simulation. We provide a novel method for reducing input model bias by recalibrating the input parameters used to drive a simulation model. A sequential quadratic programming approach with an explicit solution is provided to recalibrate the input parameters. The method is therefore computationally inexpensive. An algorithm outlining our proposed procedure is provided within the paper. An evaluation of the method shows the method successfully reduces input model bias and may also reduce the mean squared error caused by input modelling in the output of a simulation model.

Suggested Citation

  • Lucy E. Morgan & Luke Rhodes-Leader & Russell R. Barton, 2022. "Reducing and Calibrating for Input Model Bias in Computer Simulation," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2368-2382, July.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:4:p:2368-2382
    DOI: 10.1287/ijoc.2022.1183
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    References listed on IDEAS

    as
    1. Morgan, Lucy E. & Nelson, Barry L. & Titman, Andrew C. & Worthington, David J., 2019. "Detecting bias due to input modelling in computer simulation," European Journal of Operational Research, Elsevier, vol. 279(3), pages 869-881.
    2. Saurabh Bansal & Genaro J. Gutierrez & John R. Keiser, 2017. "Using Experts’ Noisy Quantile Judgments to Quantify Risks: Theory and Application to Agribusiness," Operations Research, INFORMS, vol. 65(5), pages 1115-1130, October.
    3. Russell C. H. Cheng & Jack P. C. Kleijnen, 1999. "Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses," Operations Research, INFORMS, vol. 47(5), pages 762-777, October.
    4. Y Lin & E Song & B L Nelson, 2015. "Single-experiment input uncertainty," Journal of Simulation, Taylor & Francis Journals, vol. 9(3), pages 249-259, August.
    5. Barry L. Nelson & Alan T. K. Wan & Guohua Zou & Xinyu Zhang & Xi Jiang, 2021. "Reducing Simulation Input-Model Risk via Input Model Averaging," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 672-684, May.
    6. Luís Felipe Bueno & Gabriel Haeser & Luiz-Rafael Santos, 2020. "Towards an efficient augmented Lagrangian method for convex quadratic programming," Computational Optimization and Applications, Springer, vol. 76(3), pages 767-800, July.
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