New development of optimal computing budget allocation for discrete event simulation
Proceedings of the 29th conference on Winter simulation
Improved decision processes through simultaneous simulation and time dilation
Proceedings of the 32nd conference on Winter simulation
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Fast Simulation of Rare Events in Queueing and Reliability Models
Performance Evaluation of Computer and Communication Systems, Joint Tutorial Papers of Performance '93 and Sigmetrics '93
Multilevel Splitting for Estimating Rare Event Probabilities
Operations Research
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Optimal computing budget allocation for multi-objective simulation models
WSC '04 Proceedings of the 36th conference on Winter simulation
Splitting for rare-event simulation
Proceedings of the 38th conference on Winter simulation
Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling
INFORMS Journal on Computing
Simulating network cyber attacks using splitting techniques
Proceedings of the Winter Simulation Conference
Combining simulation allocation and optimal splitting for rare-event simulation optimization
Proceedings of the Winter Simulation Conference
Simulating non-stationary congestion systems using splitting with applications to cyber security
Proceedings of the Winter Simulation Conference
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Efficiency is a big concern when using simulation to estimate rare-event probabilities, since a huge number of simulation replications may be needed in order to obtain a reasonable estimate of such a probability. Furthermore, when multiple designs must be compared, and each design requires simulation of a rare event, then the total number of samples across all designs can be prohibitively high. This paper presents a new approach to enhance the efficiency for rare-event simulation. Our approach is developed by integrating the notions of level splitting and optimal computing budget allocation. The goal is to determine the optimal numbers of simulation runs across designs and across a number of splitting levels so that the variance of the rare-event estimator is minimized.