Simulation response optimization via direct conjugate direction method

This paper modifies Powell's conjugate direction method for unconstrained, continuous, local optimization problems to adapt to the stochastic environment in simulation response optimization. The main idea underlying the proposed method is to conduct several replications at each trial point to obtain reliable estimate of the theoretical response. To avoid misjudging the real difference between two points due to the stochastic nature, a t-test of the statistical hypothesis is employed to replace the simple comparison of the mean responses. In an experimental comparison, the proposed method outperforms the Nelder–Mead simplex method, a quasi-Newton method, and several other methods in solving a stochastic Watson function with nine variables, a queueing problem with two variables, and an inventory problem with two variables.