Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Mehdad, E."'
Autor:
Mehdad, E.
Simulation is a popular tool for analyzing complex systems. However, simulation models are often difficult to build and require significant time to run. We often need to invest much money and time to use a simulation model of a complex system. To ben
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______645::64ce9918956dfa07fa59dd19bbcbb42d
https://pure.uvt.nl/portal/files/5783766/Ehsan_Mehdad_s_PhD_Dissertation.pdf
https://pure.uvt.nl/portal/files/5783766/Ehsan_Mehdad_s_PhD_Dissertation.pdf
Autor:
Mehdad, E., Kleijnen, Jack P.C.
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::32b030b65ce452cfed2bfdf507ed0795
https://research.tilburguniversity.edu/en/publications/4915047b-afe4-4fc7-8a1c-45b8537fb4ea
https://research.tilburguniversity.edu/en/publications/4915047b-afe4-4fc7-8a1c-45b8537fb4ea
Autor:
Mehdad, E., Kleijnen, Jack P.C.
In this paper we investigate global optimization for black-box simulations using metamodels to guide this optimization. As a novel metamodel we introduce intrinsic Kriging, for either deterministic or random simulation. For deterministic simulation w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::4033e70bc66b41d8c506b3d329e456b1
https://research.tilburguniversity.edu/en/publications/8fa8d96f-a086-4c4b-88ab-9c8b523beb64
https://research.tilburguniversity.edu/en/publications/8fa8d96f-a086-4c4b-88ab-9c8b523beb64
Autor:
Mehdad, E., Kleijnen, Jack P.C.
We derive intrinsic Kriging, using Matherons intrinsic random functions which eliminate the trend in classic Kriging. We formulate this intrinsic Kriging as a metamodel in deterministic and random simulation models. For random simulation we derive an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::3e95d3e1aae65cf09aa8cfb1422d9d29
https://research.tilburguniversity.edu/en/publications/9ab2e856-d971-475d-a842-de073d7b13b3
https://research.tilburguniversity.edu/en/publications/9ab2e856-d971-475d-a842-de073d7b13b3
Autor:
Kleijnen, Jack P.C., Mehdad, E.
To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or multivariate Kriging (Gaussian process) metamodels. In multivariate Kriging we face a major problem: the covariance matrix of all res
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6ecf923f4e327e3b923e2ddcb1d1456d
https://pure.uvt.nl/portal/files/1579854/2014-012.pdf
https://pure.uvt.nl/portal/files/1579854/2014-012.pdf
Autor:
Kleijnen, Jack P.C., Mehdad, E.
Publikováno v:
2013 Winter Simulations Conference (WSC).
A classic Kriging or Gaussian process (GP) metamodel estimates the variance of its predictor by plugging-in the estimated GP (hyper)parameters; namely, the mean, variance, and covariances. The problem is that this predictor variance is biased. To sol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f6464c71b3f02bcdd177d1ed44deb80
https://doi.org/10.1109/wsc.2013.6721487
https://doi.org/10.1109/wsc.2013.6721487
Autor:
Mehdad, E., Kleijnen, Jack P.C.
This paper investigates two related questions: (1) How to derive a confidence interval for the output of a combination of simulation inputs not yet simulated? (2) How to select the next combination to be simulated when searching for the optimal combi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::12ad629474ee735654b36545745f8e40
https://research.tilburguniversity.edu/en/publications/9c95782e-ca4c-44a5-893a-38edea453b6b
https://research.tilburguniversity.edu/en/publications/9c95782e-ca4c-44a5-893a-38edea453b6b
Autor:
Kleijnen, Jack P.C., Mehdad, E.
To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or multivariate Kriging (Gaussian Process) models. Univariate Kriging may use a popular MATLAB Kriging toolbox called \DACE'. Multivaria
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::12ac87bb8d7e25920b9415c043a63ef2
http://arno.uvt.nl/show.cgi?fid=122627
http://arno.uvt.nl/show.cgi?fid=122627
Autor:
Kleijnen, Jack P.C., Mehdad, E., van Beers, W.C.M., Laroque, C., Himmelspach, J., Pasupathy, R., Rose, O., Uhrmacher, A.M.
Publikováno v:
Proceedings of the 2012 Winter Simulation Conference, 543-554
STARTPAGE=543;ENDPAGE=554;TITLE=Proceedings of the 2012 Winter Simulation Conference
STARTPAGE=543;ENDPAGE=554;TITLE=Proceedings of the 2012 Winter Simulation Conference
Distribution-free bootstrapping of the replicated responses of a given discrete-event simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::18d347b6c6b0c99667262c025d41fded
https://research.tilburguniversity.edu/en/publications/972e079d-0209-45bf-b25e-ae65c56b853a
https://research.tilburguniversity.edu/en/publications/972e079d-0209-45bf-b25e-ae65c56b853a
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