Abstract
Statistical Ranking and Selection (R&S) is a collection of experiment design and analysis techniques for selecting the system with the largest or smallest mean performance from among a finite set of alternatives. R&S procedures have received considerable research attention in the stochastic simulation community, and they have been incorporated in commercial simulation software. All existing procedures assume that the set of alternatives is available at the beginning of the experiment. In many situations, however, the alternatives are revealed (generated) sequentially during the experiment. We introduce procedures that are capable of selecting the best alternative in these situations and provide the desired statistical guarantees.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 723-734 |
| Number of pages | 12 |
| Journal | IIE Transactions (Institute of Industrial Engineers) |
| Volume | 39 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 2007 |
Funding
The authors thank Dr. Sigrún Andradóttir of Georgia Tech for her ideas and assistance leading to the development of the SEU procedure, and the department editor and two referees for their insightful comments that significantly improved the quality of this paper. This research was partially supported by National Science Foundation grant number DMI-0217690, Hong Kong Research Grants Council grant number CERG 613305, and General Motors R&D.
Keywords
- Optimization via simulation
- Ranking and selection
- System design
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering