Sequential Selection with Unknown Correlation Structures

Huashuai Qu (), Ilya O. Ryzhov (), Michael C. Fu () and Zi Ding ()
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Huashuai Qu: Department of Mathematics, University of Maryland, College Park, Maryland 20742
Ilya O. Ryzhov: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Michael C. Fu: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Zi Ding: Department of Mathematics, University of Maryland, College Park, Maryland 20742

Operations Research, 2015, vol. 63, issue 4, 931-948

Abstract: We create the first computationally tractable Bayesian statistical model for learning unknown correlation structures in fully sequential simulation selection. Correlations represent similarities or differences between various design alternatives and can be exploited to extract much more information from each individual simulation. However, in most applications, the correlation structure is unknown, thus creating the additional challenge of simultaneously learning unknown mean performance values and unknown correlations. Based on our new statistical model, we derive a Bayesian procedure that seeks to optimize the expected opportunity cost of the final selection based on the value of information, thus anticipating future changes to our beliefs about the correlations. Our approach outperforms existing methods for known correlation structures in numerical experiments, including one motivated by the problem of optimal wind farm placement, where real data are used to calibrate the simulation model.

Keywords: Simulation; Statistical analysis; design of experiments; decision analysis; sequential (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (7)

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