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

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      Author(s)
      Akçay, Alp
      Biller, B.
      Date
      2014
      Source Title
      Proceedings of the 2014 Winter Simulation Conference, WSC 2014
      Print ISSN
      0891-7736
      Publisher
      IEEE
      Pages
      2124 - 2135
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      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.
      Keywords
      Graphic methods
      Confidence interval
      Dependence measures
      Dependence structures
      Empirical distribution functions
      Input distributions
      Posterior distributions
      Simulation outputs
      Statistical estimation
      Distribution functions
      Permalink
      http://hdl.handle.net/11693/28452
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/WSC.2014.7020057
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      • Department of Industrial Engineering 733
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