We propose to use a global metamodeling technique known as stochastic kriging to improve the efficiency of Discrete Optimization-via-Simulation (DOvS) algorithms. Stochastic kriging metamodel allows the DOvS algorithm to utilize all information collected during the optimization process and identify solutions that are most likely to lead to significant improvement in solution quality. We call the approach Stochastic Kriging for OPtimization Efficiency (SKOPE). In this paper, we integrate SKOPE with a locally convergent DOvS algorithm known as Adaptive Hyperbox Algorithm (AHA). Numerical experiments show that SKOPE significantly improves the performance of AHA in the early stage of optimization, which is very helpful for DOvS applications where the number of simulations for an optimization task is severely limited due to a short decision time window and time-consuming simulation. INTRODUCTIONIn the past decade, there has been a fast growing body of literature on how to optimize the design of a system using a simulation model. We refer to such problems as Optimization via Simulation (OvS). See Fu (2002) andFu, Glover, andApril (2005) for a review of OvS. When the decision variables are discrete valued, such as the stocking levels of products in a multi-product inventory management problem, we refer to it as Discrete Optimization via Simulation (DOvS).When there are only a few hundreds feasible solutions, ranking-and-selection algorithms can be applied to choose the best solution. Examples include the indifference-zone procedure of Nelson et al. (2001) and Kim and Nelson (2001), the Bayesian procedure of Chick and Inoue (2001), and the Optimal Computing Budget Allocation (OCBA) procedure of Chen et al. (2000). Most recently, Frazier (2012) proposed an indifference-zone procedure for more than 15,000 alternatives. However, in a typical DOvS problem, the feasible solution space often includes millions and even billions of feasible solutions and thus rankingand-selection procedures are not directly applicable.Adaptive random search has been the dominant paradigm for designing DOvS algorithms when the solution space is large. Most existing DOvS algorithms focus on asymptotic global convergence, including the stochastic ruler algorithm of Yan and Mukai (1992), the simulated annealing algorithm of Alrefaei and Andradóttir (1999), and the nested partitions algorithm of Shi and Ólafsson (2000) and Pichitlamken and Nelson (2003). These algorithms essentially have to visit every solution to guarantee global convergence and lack the efficiency necessary to solve real world problems.Another class of adaptive random search DOvS algorithms guarantees convergence to a local optimal solution. Andradóttir (1995) proposed a locally convergent algorithm for one-dimensional DOvS problems. The COMPASS algorithm of Hong and Nelson (2006) and the AHA algorithm of Xu, Hong, and Nelson (2011) also belong to this class of DOvS algorithms, but they are not restricted to one dimension. By focusing on finding a local optimum, these algorithms ca...

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