Please use this identifier to cite or link to this item:
Title: An efficient simulation budget allocation method incorporating regression for partitioned domains
Authors: Brantley, M.W.
Lee, L.H. 
Chen, C.-H.
Xu, J.
Keywords: Budget allocation
Issue Date: 2014
Citation: Brantley, M.W., Lee, L.H., Chen, C.-H., Xu, J. (2014). An efficient simulation budget allocation method incorporating regression for partitioned domains. Automatica 50 (5) : 1391-1400. ScholarBank@NUS Repository.
Abstract: Simulation can be a very powerful tool to help decision making in many applications but exploring multiple courses of actions can be time consuming. Numerous ranking and selection (R&S) procedures have been developed to enhance the simulation efficiency of finding the best design. To further improve efficiency, one approach is to incorporate information from across the domain into a regression equation. However, the use of a regression metamodel also inherits some typical assumptions from most regression approaches, such as the assumption of an underlying quadratic function and the simulation noise is homogeneous across the domain of interest. To extend the limitation while retaining the efficiency benefit, we propose to partition the domain of interest such that in each partition the mean of the underlying function is approximately quadratic. Our new method provides approximately optimal rules for between and within partitions that determine the number of samples allocated to each design location. The goal is to maximize the probability of correctly selecting the best design. Numerical experiments demonstrate that our new approach can dramatically enhance efficiency over existing efficient R&S methods. © 2014 Elsevier Ltd. All rights reserved.
Source Title: Automatica
ISSN: 00051098
DOI: 10.1016/j.automatica.2014.03.011
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jul 15, 2021


checked on Jul 10, 2019

Page view(s)

checked on Jul 14, 2021

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.