Input uncertainty in stochastic simulations in the presence of dependent discrete input variables

A. Akcay, B. Biller

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Abstract

This paper considers stochastic simulations with correlated input random variables having NORmal-To-Anything (NORTA) distributions. We assume that the simulation analyst does not know the marginal distribution functions and the base correlation matrix of the NORTA distribution but has access to a finite amount of input data for statistical inference. We propose a Bayesian procedure that decouples the input model estimation into two stages and overcomes the problem of inconsistently estimating the base correlation matrix of the NORTA distribution in the presence of discrete input variables. It further allows us to estimate the variability of the simulation output data that are attributable to the input uncertainty due to not knowing the NORTA distribution. Using this input uncertainty estimate, we introduce a simple yet effective method to obtain input uncertainty adjusted credible intervals. We illustrate our method in an assemble-to-order production system with a correlated demand arrival process.

Original languageEnglish
Pages (from-to)295-306
Number of pages12
JournalJournal of Simulation
Volume12
Issue number4
Early online date14 Dec 2017
DOIs
Publication statusPublished - 2 Oct 2018

Keywords

  • Multivariate input modeling
  • input uncertainty
  • simulation output analysis

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