An artificial neural network meta-model for constrained simulation optimization

This paper presents artificial neural network (ANN) meta‐models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta‐models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non‐linear nature of the ANN, the SO problem is iteratively approximated via non‐linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane‐relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition is met. To study the robustness and efficiency of the proposed algorithm, a realistic inventory model is used; the results are compared with those of the OptQuest optimization package. These numerical results indicate that the proposed meta‐model‐based algorithm performs quite competitively while requiring slightly fewer simulation observations.