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  1. ; (, Proceedings of the 2021 Winter Simulation Conference)
    Kim, S.; Feng, B.; Smith, K.; Masoud, S.; Zheng, Z.; Szabo, C.; Loper, M. (Ed.)
    Full Text Available 
  2. (, Proceedings of the Winter Simulation Conference)
    Kim, S.; Feng, B.; Smith, K.; Masoud, S; Zheng, Z.; Szabo, C.; Loper, M. (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ; (, Proceedings of the 2019 Winter Simulation Conference)
    N. Mustafee, N; Bae, K.-H.G.; Lazarova-Molnar, Rabe; Szabo, C; Haas, P; Son, Y-J (Ed.)
    Simple question: How sensitive is your simulation output to the variance of your simulation input models? Unfortunately, the answer is not simple because the variance of many standard parametric input distributions can achieve the same change in multiple ways as a function of the parameters. In this paper we propose a family of output-mean-with-respect-to-input-variance sensitivity measures and identify two particularly useful members of it. A further benefit of this family is that there is a straightforward estimator of any member with no additional simulation effort beyond the nominal experiment. A numerical example is provided to illustrate the method and interpretation of results. 
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