Optimal Subset Selection of Stochastic Model Using Statistical Hypothesis Test

This paper proposes an improved algorithm for the optimal subset selection of a stochastic simulation model. The algorithm uses a statistical hypothesis test based on frequentist inference to evaluate the uncertainty about the selection, and it distributes simulation resources to designs for minimizing the uncertainty in each iteration. Several experiments demonstrate the improved performance compared to the other algorithms, and the performance increases significantly as the noise of the model increases. As a result, its high robustness to noise allows the algorithm to efficiently analyze real-world problems.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-04
Language
English
Article Type
Article
Keywords

SIMULATION BUDGET ALLOCATION; OPPORTUNITY COST; OPTIMIZATION; SYSTEMS

Citation

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, v.48, no.4, pp.557 - 564

ISSN
2168-2216
DOI
10.1109/TSMC.2016.2608982
URI
http://hdl.handle.net/10203/241313
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
  • Hit : 30
  • Download : 0
  • Cited 0 times in thomson ci
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡClick to seewebofscience_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0