An algorithm to reduce the number of simulation steps for optimizing closed Jackson network

For optimizing the closed Jackson network, the gradient information obtained from perturbation analysis is a useful search method. However, in the simulation-based optimization, too many simulation steps is the major factor of reducing the optimization speed. We suggest a new method which uses the information derived from the simulation of old parameters to greatly enhance the speed of optimization