A modification of the stochastic ruler method for discrete stochastic optimization

We propose a modified stochastic ruler method for finding a global optimal solution to a discrete optimization problem in which the objective function cannot be evaluated analytically but has to be estimated or measured. Our method generates a Markov chain sequence taking values in the feasible set of the underlying discrete optimization problem; it uses the number of visits this sequence makes to the different states to estimate the optimal solution. We show that our method is guaranteed to converge almost surely (a.s.) to the set of global optimal solutions. Then, we show how our method can be used for solving discrete optimization problems where the objective function values are estimated using either transient or steady-state simulation. Finally, we provide some numerical results to check the validity of our method and compare its performance with that of the original stochastic ruler method.