Name
Abstract | ||
|---|---|---|
In this study, we consider ranking and selection problems where the simulation model is subject to input uncertainty. Under the input uncertainty, we compare system designs based on their worst-case performance, and seek to maximize the probability of selecting the design with the best performance under the worst-case scenario. By approximating the probability of correct selection (PCS), we develop an asymptotically (as the simulation budget goes to infinity) optimal solution of the resulting problem. An efficient selection procedure is designed within the optimal computing budget allocation (OCBA) framework. Numerical tests show the high efficiency of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WSC.2016.7822146 | Winter Simulation Conference |
Keywords | Field | DocType |
optimal computing budget allocation,input uncertainty,ranking,selection problems,simulation model,worst-case performance,worst-case scenario,probability of correct selection,PCS,OCBA framework,numerical tests | Resource management,Numerical tests,Mathematical optimization,Ranking,Numerical models,Computer science,Optimal computing budget allocation,Infinity,Sensitivity analysis,Robustness (computer science) | Conference |
ISSN | ISBN | Citations |
0891-7736 | 978-1-5090-4484-9 | 0 |
PageRank | References | Authors |
0.34 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyang Gao | 1 | 80 | 11.83 |
| Hui Xiao | 2 | 1 | 0.69 |
| Enlu Zhou | 3 | 112 | 22.25 |
| Weiwei Chen | 4 | 125 | 12.21 |