Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Some topics for simulation optimization
Proceedings of the 40th Conference on Winter Simulation
The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
Simulation optimization using the cross-entropy method with optimal computing budget allocation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Simulation model calibration with correlated knowledge-gradients
Winter Simulation Conference
Hierarchical Knowledge Gradient for Sequential Sampling
The Journal of Machine Learning Research
Consistency of Sequential Bayesian Sampling Policies
SIAM Journal on Control and Optimization
Sequential Sampling with Economics of Selection Procedures
Management Science
Combining simulation allocation and optimal splitting for rare-event simulation optimization
Proceedings of the Winter Simulation Conference
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Ordinal optimization offers an efficient approach for simulation optimization by focusing on ranking and selecting a finite set of good alternatives. Because simulation replications only give estimates of the performance of each alternative, there is a potential for incorrect selection. Two measures of selection quality are the alignment probability or the probability of correct selection (P{CS}), and the expected opportunity cost E[OC], of a potentially incorrect selection. Traditional ordinal optimization approaches focus on the former case. This paper extends Chen's optimal computing budget allocation (OCBA) approach, which allocated replications to improve P{CS}, to provide the first OCBA-like procedure that optimizes E[OC] in some sense. The procedure performs efficiently in numerical experiments.