Name
Abstract | ||
|---|---|---|
This paper summarizes a new approach that we recently proposed for ranking and selection problems, one that maximizes the expected NPV of decisions made when using stochastic or discrete-event simulation. The expected NPV models not only the economic benefit from implementing a selected system, but also the marginal costs of simulation runs and discounting due to simulation analysis time. Our formulation assumes that facilities exist to simulate a fixed number of alternative systems, and we pose the problem as a "stoppable" Bayesian bandit problem. Under relatively general conditions, a Gittins index can be used to indicate which system to simulate or implement. We give an asymptotic approximation for the index that is appropriate when simulation outputs are normally distributed with known but potentially different variances for the different systems. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/WSC.2006.323084 | Winter Simulation Conference |
Keywords | Field | DocType |
Bayes methods,discrete event simulation,economics,Bayesian bandit problem,Gittins index,asymptotic approximation,discrete-event simulation,economic analysis,simulation selection problems,stochastic simulation | Mathematical optimization,Discounting,Ranking,Economic analysis,Computer science,Simulation,Gittins index,Marginal cost,Discrete event simulation,Bayesian probability | Conference |
ISBN | Citations | PageRank |
1-4244-0501-7 | 2 | 0.42 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen E. Chick | 1 | 1127 | 152.40 |
| Noah Gans | 2 | 613 | 66.60 |