Stochastic Kriging-Based Optimization Applied in Direct Policy Search for Decision Problems in Infrastructure Planning

In this paper, we apply a stochastic Kriging-based optimization algorithm to solve a generic infrastructure planning problem using direct policy search (DPS) as a heuristic approach. Such algorithms are particularly effective at handling high computational cost optimization, especially the sequential Kriging optimization (SKO). SKO has been proving to be well-suited to deal with noise or uncertainty problems, whereas assumes heterogeneous simulation noise and explores both intrinsic uncertainty inherent in a stochastic simulation and extrinsic uncertainty about the unknown response surface. Additionally, this paper employs a recent stochastic Kriging method that incorporates smoothed variance estimations through a deterministic Kriging metamodel. The problem evaluated is the DPS as a heuristic approach, this is a sequential decision problem-solving method that will be applied to a generic infrastructure planning problem under uncertainty. Its performance depends on system and cost model parameters. Previous research has employed Cross Entropy (CE) as a global optimization method for DPS, while this paper utilizes SKO as a stochastic Kriging-based optimization method and compares the results with those obtained by CE. The proposed approach demonstrates promising results and has the potential to advance the field of Kriging-based algorithms to solve engineering problems under uncertainties.