TY - JOUR
T1 - A simulation budget allocation procedure for finding both extreme designs simultaneously in discrete-event simulation
AU - Choi, Seon Han
AU - Choi, Changbeom
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT) under Grant 2019R1G1A1098951.
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Although discrete-event simulation has been widely used in various engineering fields, its efficiency remains an issue. Ranking and selection (RS) procedures can solve this efficiency problem by allocating a limited simulation budget intelligently. While the existing RS procedures mostly aim to find only the best simulation input design, practitioners sometimes require the worst design as well to analyze systems requiring high reliability, such as military systems, municipal waste management, etc. Motivated by these practical needs, we propose a simulation budget allocation procedure for selecting both extreme designs simultaneously in the presence of large stochastic noise. To maximize the accuracy of the selections under a limited budget, the proposed procedure sequentially allocates a small budget and updates the simulation results such that they can be used as significant evidence for the correct selections. Our experimental results on benchmark and practical problems demonstrate improved efficiency compared to previous works. It is expected that the proposed procedure will be effectively utilized in the fields of the fourth industrial revolution, such as digital twins that demand quickly finding both extreme designs to maintain synchronization with the corresponding real systems.
AB - Although discrete-event simulation has been widely used in various engineering fields, its efficiency remains an issue. Ranking and selection (RS) procedures can solve this efficiency problem by allocating a limited simulation budget intelligently. While the existing RS procedures mostly aim to find only the best simulation input design, practitioners sometimes require the worst design as well to analyze systems requiring high reliability, such as military systems, municipal waste management, etc. Motivated by these practical needs, we propose a simulation budget allocation procedure for selecting both extreme designs simultaneously in the presence of large stochastic noise. To maximize the accuracy of the selections under a limited budget, the proposed procedure sequentially allocates a small budget and updates the simulation results such that they can be used as significant evidence for the correct selections. Our experimental results on benchmark and practical problems demonstrate improved efficiency compared to previous works. It is expected that the proposed procedure will be effectively utilized in the fields of the fourth industrial revolution, such as digital twins that demand quickly finding both extreme designs to maintain synchronization with the corresponding real systems.
KW - Discrete-event simulation
KW - extreme designs selection
KW - ranking and selection
KW - simulation-based optimization
KW - stochastic system
UR - http://www.scopus.com/inward/record.url?scp=85085963895&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2995196
DO - 10.1109/ACCESS.2020.2995196
M3 - Article
AN - SCOPUS:85085963895
SN - 2169-3536
VL - 8
SP - 93978
EP - 93986
JO - IEEE Access
JF - IEEE Access
M1 - 9094674
ER -