R-SPLINE for local integer-ordered simulation optimization problems with stochastic constraints

R-SPLINE is a recently proposed competitor to the popular COMPASS algorithm for solving local integer-ordered simulation optimization problems that have either an unconstrained or a deterministically-constrained feasible region. R-SPLINE is a refined sample-average approximation algorithm with a structure that is particularly conducive to the inclusion of stochastic constraints. In this paper we consider one such trivial adaptation of R-SPLINE. Our aim is narrow in that we wish only to investigate the asymptotic behavior of the resulting iterates. Accordingly, we demonstrate sufficient conditions under which the proposed adaptation's iterates match the consistency and convergence rate qualities of the iterates from the originally proposed R-SPLINE. Ongoing numerical experiments show much promise but raise important questions about the choice of algorithm parameters when the adaptation is executed on problems where one or more of the constraints are binding.