ABSTRACT This paper presents a lognormal ordinary kriging (LOK) metamodel algorithm and its application to optimize a stochastic simulation problem. Kriging models have been developed as an interpolation method in geology. They have been successfully used for the deterministic simulation optimization (SO) problem. In recent years, kriging metamodeling has attracted a growing interest with stochastic problems.SO researchers have begun using ordinary kriging through global optimization in stochastic systems. The goals of this study are to present LOK metamodel algorithm and to analyze the result of the application step-by-step. The results show that LOK is a powerful alternative metamodel in simulation optimization when the data are too skewed.