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A DFO technique to calibrate queueing models
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DOI：10.1016/j.cor.2009.04.020.png)
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Abstract
En 中文
A crucial step in the modeling of a system is to determine the values of the parameters to use in the model. In this paper we assume that we have a set of measurements collected from an operational system, and that an appropriate model of the system(e.g., based on queueing theory) has been developed. Not in frequently proper values for certain parameters of this model may be difficult to estimate from available data(because the corresponding parameters have unclear physical meaning or because they cannot be directly obtained from available measurements, etc.). Hence, we need a technique to determine the missing parameter values, i.e., to calibrate the model. As an alternative to unscalable bruteforce technique, we propose to view model calibration as a nonlinear optimization problem with constraints. The resulting method isconceptually simple and easy to implement. Our contribution is twofold. First, we propose improved definitions of the objective function to quantify the distance between performance in dices produced by the model and the values obtained from measurements. Second, we develop a customized derivative-free optimization (DFO) technique whose original feature is the ability to allow temporary constraint violations. This technique allows us to solve this optimization problem accurately, thereby providing the right parameter values. we illustrate our method using two simple real-life case studies. (c) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Calibration
Queueing model
Derivative-free optimization
Objective function
Optimization
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