An artificial neural network meta-model for constrained simulation optimization

作者：Nezhad Ali Mohammad^*; Mahlooji Hashem

来源：Journal of the Operational Research Society, 2014, 65(8): 1232-1244.

DOI：10.1057/jors.2013.73

• expensive simulation optimization
• meta-model-based algorithm
• ANN
• SNOPT solver

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.

• 出版日期2014-8