TY - JOUR
T1 - On the optimal design of the randomized unbiased Monte Carlo estimators
AU - Cui, Zhenyu
AU - Lee, Chihoon
AU - Zhu, Lingjiong
AU - Zhu, Yunfan
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/7
Y1 - 2021/7
N2 - We consider a class of unbiased Monte Carlo estimators and develop an efficient algorithm to produce the distribution of the optimal random truncation level. We establish the convergence and optimality results of the associated algorithm and also derive its exact complexity. We find this algorithm has a much lower complexity as compared to the existing one in the literature. The proposed algorithm is also applicable to optimization problems arising in supply chain management, such as economic reorder interval problem.
AB - We consider a class of unbiased Monte Carlo estimators and develop an efficient algorithm to produce the distribution of the optimal random truncation level. We establish the convergence and optimality results of the associated algorithm and also derive its exact complexity. We find this algorithm has a much lower complexity as compared to the existing one in the literature. The proposed algorithm is also applicable to optimization problems arising in supply chain management, such as economic reorder interval problem.
KW - Simulation
KW - Simulation optimization
KW - Unbiased estimation
UR - http://www.scopus.com/inward/record.url?scp=85106263630&partnerID=8YFLogxK
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U2 - 10.1016/j.orl.2021.05.004
DO - 10.1016/j.orl.2021.05.004
M3 - Article
AN - SCOPUS:85106263630
SN - 0167-6377
VL - 49
SP - 477
EP - 484
JO - Operations Research Letters
JF - Operations Research Letters
IS - 4
ER -