TY - JOUR
T1 - Efficient simulation budget allocation for subset selection using regression metamodels
AU - Gao, Fei
AU - Shi, Zhongshun
AU - Gao, Siyang
AU - Xiao, Hui
PY - 2019/8
Y1 - 2019/8
N2 - This research considers the ranking and selection (R&S)problem of selecting the optimal subset from a finite set of alternative designs. Given the total simulation budget constraint, we aim to maximize the probability of correctly selecting the top-m designs. In order to improve the selection efficiency, we incorporate the information from across the domain into regression metamodels. In this research, we assume that the mean performance of each design is approximately quadratic. To achieve a better fit of this model, we divide the solution space into adjacent partitions such that the quadratic assumption can be satisfied within each partition. Using the large deviation theory, we propose an approximately optimal simulation budget allocation rule in the presence of partitioned domains. Numerical experiments demonstrate that our approach can enhance the simulation efficiency significantly.
AB - This research considers the ranking and selection (R&S)problem of selecting the optimal subset from a finite set of alternative designs. Given the total simulation budget constraint, we aim to maximize the probability of correctly selecting the top-m designs. In order to improve the selection efficiency, we incorporate the information from across the domain into regression metamodels. In this research, we assume that the mean performance of each design is approximately quadratic. To achieve a better fit of this model, we divide the solution space into adjacent partitions such that the quadratic assumption can be satisfied within each partition. Using the large deviation theory, we propose an approximately optimal simulation budget allocation rule in the presence of partitioned domains. Numerical experiments demonstrate that our approach can enhance the simulation efficiency significantly.
KW - OCBA
KW - Ranking and selection
KW - Regression
KW - Simulation optimization
KW - Subset selection
UR - http://www.scopus.com/inward/record.url?scp=85065760838&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85065760838&origin=recordpage
U2 - 10.1016/j.automatica.2019.05.022
DO - 10.1016/j.automatica.2019.05.022
M3 - RGC 21 - Publication in refereed journal
VL - 106
SP - 192
EP - 200
JO - Automatica
JF - Automatica
SN - 0005-1098
ER -