An Improved Budget Allocation Procedure for Selecting the Best-Simulated Design in the Presence of Large Stochastic Noise

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Motivated by the increasing practical needs for simulation optimization of modern industrial systems, this paper proposes an efficient ranking and selection (R&S) procedure for selecting the best-simulated design from a finite set of alternatives in the presence of large stochastic noise. To obtain the correct selection under a limited simulation budget, the proposed procedure sequentially allocates the budget to minimize the evaluated uncertainty values of the selection through a two-step process based on the existing uncertainty evaluation (UE) procedure. This two-step process reduces the inefficiency of the underlying UE procedure while keeping its high robustness to noise, thereby achieving improved the efficiency for the proposed procedure in a noisy environment. This improved efficiency is demonstrated in comparative experiments with other R&S procedures on several benchmark problems. In particular, the experimental results of three practical optimization problems emphasize the necessity of the proposed procedure.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-11
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.7, pp.154435 - 154446

ISSN
2169-3536
DOI
10.1109/ACCESS.2019.2948980
URI
http://hdl.handle.net/10203/272631
Appears in Collection
EE-Journal Papers(저널논문)
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