PROPAGATION OF EPISTEMIC UNCERTAINTY IN QUEUEING MODELS WITH UNRELIABLE SERVER USING CHAOS EXPANSIONS - Laboratoire de Mathematiques Accéder directement au contenu
Article Dans Une Revue Communications in Statistics - Simulation and Computation Année : 2021

PROPAGATION OF EPISTEMIC UNCERTAINTY IN QUEUEING MODELS WITH UNRELIABLE SERVER USING CHAOS EXPANSIONS

Résumé

In this paper, we develop a numerical approach based on Chaos expansions to analyze the sensitivity and the propagation of epistemic uncertainty through a queueing systems with breakdowns. Here, the quantity of interest is the stationary distribution of the model, which is a function of uncertain parameters. Polynomial chaos provide an efficient alternative to more traditional Monte Carlo simulations for modelling the propagation of uncertainty arising from those parameters. Furthermore, Polynomial chaos expansion affords a natural framework for computing Sobol' indices. Such indices give reliable information on the relative importance of each uncertain entry parameters. Numerical results show the benefit of using Polynomial Chaos over standard Monte-Carlo simulations, when considering statistical moments and Sobol' indices as output quantities.
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Dates et versions

hal-01522435 , version 1 (15-05-2017)

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Katia Bachi, Cédric Chauvière, Hacène Djellout, Karim Abbas. PROPAGATION OF EPISTEMIC UNCERTAINTY IN QUEUEING MODELS WITH UNRELIABLE SERVER USING CHAOS EXPANSIONS. Communications in Statistics - Simulation and Computation, 2021, Communications in Statistics - Simulation and Computation, 50 (4), pp.1019-1041. ⟨hal-01522435⟩
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