Name
Abstract | ||
|---|---|---|
In this paper, we consider the ranking and selection (R&S) problem with input uncertainty. It seeks to maximize the probability of correct selection (PCS) for the best design under a fixed simulation budget, where each design is measured by their worst-case performance. To simplify the complexity of PCS, we develop an approximated probability measure and derive an asymptotically optimal solution of the resulting problem. An efficient selection procedure is then designed within the optimal computing budget allocation (OCBA) framework. More importantly, we provide some useful insights on characterizing an efficient robust selection rule and how it can be achieved by adjusting the simulation budgets allocated to each scenario. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.automatica.2017.03.019 | Automatica |
Keywords | Field | DocType |
Simulation optimization,Ranking and selection,OCBA,Robust optimization,Computing budget allocation | Mathematical optimization,Ranking,Computer science,Robust optimization,Optimal computing budget allocation,Probability measure,Asymptotically optimal algorithm | Journal |
Volume | Issue | ISSN |
81 | 1 | 0005-1098 |
Citations | PageRank | References |
3 | 0.40 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyang Gao | 1 | 80 | 11.83 |
| Hui Xiao | 2 | 29 | 6.96 |
| Enlu Zhou | 3 | 112 | 22.25 |
| Weiwei Chen | 4 | 125 | 12.21 |