Discrete Optimization via Simulation; Value of Information; Kriging; Correlated Samples;
The authors consider optimization via simulation over a finite set of alternatives. The authors employ a Bayesian value-of-information approach in which they allow both correlated prior beliefs on the sampling means and correlated sampling. Correlation in the prior belief allow us to learn about an alternative’s value from samples of similar alternatives.Correlation in sampling, achieved through common random numbers, allows us to reduce the variance in comparing one alternative to another. The authors allow for a more general combination of both types of correlation than has been offered previously in the Bayesian ranking and selection literature.The authors do so by giving an exact expression for the value of information for sampling the difference between a pair of alternatives, and derive new knowledge-gradient methods based on this valuation.