Abstract
We consider the ranking and selection (R&S) problem with fixed simulation budget, in which the budget is assumed to be allocated sequentially. Deriving the optimal sampling procedure for this problem amounts to solving a stochastic dynamic program that is highly intractable. To overcome this difficulty, the existing R&S procedures are often designed from a myopic viewpoint. However, these myopic procedures are only single-step optimal and may have a poor performance for general sequential R&S problems. Therefore, in this paper, we combine two popular lookahead strategies and design a non-myopic knowledge gradient (KG) procedure. Meanwhile, to streamline the computation of procedure, we propose a modified Monte Carlo tree search method specifically designed under the R&S context. We show that the new procedure can exhibit a performance superior to the classic KG.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2022 Winter Simulation Conference, WSC 2022 |
| Editors | B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3051-3062 |
| Number of pages | 12 |
| ISBN (Electronic) | 9798350309713 |
| DOIs | |
| State | Published - 2022 |
| Externally published | Yes |
| Event | 2022 Winter Simulation Conference, WSC 2022 - Guilin, China Duration: Dec 11 2022 → Dec 14 2022 |
Publication series
| Name | Proceedings - Winter Simulation Conference |
|---|---|
| Volume | 2022-December |
| ISSN (Print) | 0891-7736 |
Conference
| Conference | 2022 Winter Simulation Conference, WSC 2022 |
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| Country/Territory | China |
| City | Guilin |
| Period | 12/11/22 → 12/14/22 |
Bibliographical note
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