Quantifying input uncertainty in an assemble-to-order system simulation with correlated input variables of mixed types

A.E. Akçay, B. Biller

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
3 Downloads (Pure)

Abstract

We consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates random input vectors with correlated discrete and continuous components. In this paper, we capture the dependence between input variables in an undirected graphical model and decouple the statistical estimation of the univariate input distributions and the underlying dependence measure into separate problems. The estimation errors due to finiteness of the real-world data introduce the so-called input uncertainty in the simulation output. We propose a method that accounts for input uncertainty by sampling the univariate empirical distribution functions via bootstrapping and by maintaining a posterior distribution of the precision matrix that corresponds to the dependence structure of the graphical model. The method improves the coverages of the confidence intervals for the expected profit the per period.
Original languageEnglish
Title of host publicationProceedings of the 2014 Winter Simulation Conference, 7-10 September 2014, Savanah, Georgia
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2124-2135
ISBN (Print)978-1-4799-7484-9
DOIs
Publication statusPublished - 2014

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