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Sensitivity analysis of queueing models based on polynomial chaos approach
Lounes Ameur1, Lahcene Bachioua2
1Department of Technology, 20 August 1955 University of Skikda, Skikda, Algeria.
Arabian Journal of Mathematics
|November 19, 2021
Summary
This study introduces a novel sensitivity analysis for queueing models with uncertain parameters. The polynomial chaos expansion method effectively quantifies parameter influence on system performance.
Area of Science:
- Operations Research
- Applied Mathematics
- Stochastic Processes
Background:
- Queueing systems rely on numerous input parameters with inherent uncertainties.
- Accurate analysis necessitates accounting for parameter variability in model outputs.
Purpose of the Study:
- To develop a sensitivity analysis method for queueing models with Gaussian-distributed parameters.
- To quantify the impact of parameter uncertainty on model performance metrics.
Main Methods:
- Utilizing polynomial chaos expansion to decompose model outputs (stationary distributions).
- Deriving sensitivity indices directly from expansion coefficients.
- Applying the method to the M/G/1/N queueing model.
Main Results:
- Identified the most influential parameters in the M/G/1/N queueing model.
- Demonstrated the accuracy of the polynomial chaos approach through numerical examples.
- Validated results against Monte Carlo simulations.
Conclusions:
- The proposed sensitivity analysis provides an accurate and efficient tool for understanding parameter influence in queueing systems.
- This method aids practitioners in various scientific and engineering fields by clarifying uncertainty impacts.
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