Ranking and selection with unknown correlation structures

Authors:
Huashuai Qu;Ilya O. Ryzhov;Michael C. Fu
Affiliations:
University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD
Venue:
Proceedings of the Winter Simulation Conference
Year:
2012

Citing 7
Cited 0

Using common random numbers for indifference-zone selection and multiple comparisons in simulation

Management Science
New Procedures to Select the Best Simulated System Using Common Random Numbers

Management Science
Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling

INFORMS Journal on Computing
Sequential Sampling to Myopically Maximize the Expected Value of Information

INFORMS Journal on Computing
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation

Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
A brief introduction to optimization via simulation

Winter Simulation Conference
Calibrating simulation models using the knowledge gradient with continuous parameters

Proceedings of the Winter Simulation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

We create the first computationally tractable Bayesian statistical model for learning unknown correlations among estimated alternatives in fully sequential ranking and selection. Although correlations allow us to extract more information from each individual simulation, the correlation structure is itself unknown, and we face the additional challenge of simultaneously learning the unknown values and unknown correlations from simulation. We derive a Bayesian procedure that allocates simulations based on the value of information, thus exploiting the correlation structure and anticipating future changes to our beliefs about the correlations. We test the model and algorithm in a simulation study motivated by the problem of optimal wind farm placement, and obtain encouraging empirical results.