مدل سازی ورودی در شبیه سازی رایانه ای
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مدل سازی ورودی در شبیه سازی رایانه ای

عنوان فارسی مقاله: تشخیص گرایش به دلیل مدل سازی ورودی در شبیه سازی رایانه ای
عنوان انگلیسی مقاله: Detecting bias due to input modelling in computer simulation
مجله/کنفرانس: مجله اروپایی درباره تحقیقات عملیاتی – European Journal of Operational Research
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات، معماری سیستم های کامپیوتری
کلمات کلیدی فارسی: شبیه سازی، گرایش، عدم قطعیت، مدلسازی ورودی
کلمات کلیدی انگلیسی: Simulation، Bias، Uncertainty، Input modelling
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ejor.2019.06.003
دانشگاه: Statistics and Operational Research Centre for Doctoral Training in Partnership with Industry (STOR-i) Lancaster University Lancaster, LA1 4YR, UK
صفحات مقاله انگلیسی: 13
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.712 در سال 2018
شاخص H_index: 226 در سال 2019
شاخص SJR: 2.205 در سال 2018
شناسه ISSN: 0377-2217
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E13526
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Background

3. Detecting bias of a relevant size

4. Empirical evaluation

5. Conclusion

Acknowledgements

Appendix A. Variability of the Jackknife estimator of bias

Appendix B. Asymptotics of b and bapprox

Appendix C. Asymptotics of 

References

بخشی از مقاله (انگلیسی)

Abstract

This is the first paper to approach the problem of bias in the output of a stochastic simulation due to using input distributions whose parameters were estimated from real-world data. We consider, in particular, the bias in simulation-based estimators of the expected value (long-run average) of the real-world system performance; this bias will be present even if one employs unbiased estimators of the input distribution parameters due to the (typically) nonlinear relationship between these parameters and the output response. To date this bias has been assumed to be negligible because it decreases rapidly as the quantity of real-world input data increases. While true asymptotically, this property does not imply that the bias is actually small when, as is always the case, data are finite. We present a delta-method approach to bias estimation that evaluates the nonlinearity of the expected-value performance surface as a function of the input-model parameters. Since this response surface is unknown, we propose an innovative experimental design to fit a response-surface model that facilitates a test for detecting a bias of a relevant size with specified power. We evaluate the method using controlled experiments, and demonstrate it through a realistic case study concerning a healthcare call centre.

Introduction

In stochastic simulation the “stochastic” element of the simulation comes from the input models that drive it. In this paper we focus on parametric input models, probability distributions or stochastic processes that are estimated from observations of the real-world system of interest. Since we can only ever collect a finite number of observations, error, with respect to what the simulation says about the real-world system performance, is inevitable. In this paper ‘response’ means the expected value of a simulated output performance measure. Error caused by input modelling can be broken down as MSE = Variance + Bias2; that is, the mean squared error (MSE) due to input modelling is made up of the variability of the simulation response caused by input modelling, known in the literature as input uncertainty variance (IU variance), and the squared bias due to input modelling. Barton (2012) explains that, even in very reasonable simulation scenarios, analysis of the response of interest can be very different when error due to input modelling is included. Barton (2012) was referring to the IU variance, but the same idea holds for the bias due to input modelling. In simulation models where a large number of replications of the simulation are completed, effectively driving out the inherent simulation noise caused by random-variate generation, ignoring the input modelling uncertainty can lead to overconfidence in the simulation results. Underestimating the error of the simulation response is dangerous, especially when this output may be used to guide important decisions about a real-world system.