Restricted subset selection procedures for simulation
Operations Research
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Using Ordinal Optimization Approach to Improve Efficiency of Selection Procedures
Discrete Event Dynamic Systems
Comparison with a Standard via All-Pairwise Comparisons
Discrete Event Dynamic Systems
Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
Indifference-zone subset selection procedures: using sample means to improve efficiency
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
ADAPT Selection procedures to process correlated and non-normal data with batch means
Winter Simulation Conference
| Hi-index | 0.00 |
This paper develops procedures for selecting a set of normal populations with unknown means and unknown variances in order that the final subset of selected populations satisfies the following requirements: with probability at least P*, the selected subset will contain a population or "only and all" of those populations whose mean lies less than the distance d* from the smallest mean. The size of the selected subset is random, however, at most m populations will finally be chosen. A restricted subset attempts to exclude populations that are deviated more than d* from the smallest mean. Here P*, d*, and m are users specified parameters. The procedure can be used when the unknown variances across populations are unequal. An experimental performance evaluation demonstrates the validity and efficiency of these restricted subset selection procedures.