Global-Local Metamodel Assisted Two-Stage Optimization via Simulation

TL;DR

This paper introduces a global-local metamodel approach to enhance two-stage stochastic programming with simulation, improving decision-making efficiency and accuracy in complex systems with unknown responses.

Contribution

It develops a novel global-local metamodel framework that efficiently guides the iterative search for optimal decisions in two-stage stochastic programming with simulation.

Findings

Achieves substantial efficiency improvements in decision optimization.

Ensures convergence to the optimal solution in complex stochastic systems.

Demonstrates high accuracy in empirical studies.

Abstract

To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted two-stage optimization via simulation that can efficiently employ the simulation resource to iteratively solve for the optimal first- and second-stage decisions. Specifically, at each visited first-stage decision, we develop a local metamodel to simultaneously solve a set of scenario-based second-stage optimization problems, which also allows us to estimate the optimality gap. Then, we construct a global metamodel accounting for the errors induced by: (1) using a finite number of scenarios to approximate the expected future cost occurring in the planning horizon, (2) second-stage optimality gap, and (3) finite visited first-stage decisions. Assisted by the global-local metamodel, we propose a new simulation optimization approach that can efficiently and iteratively search for the optimal first- and second-stage decisions. Our framework can guarantee the convergence of optimal solution for the discrete two-stage optimization with unknown objective, and the empirical study indicates that it achieves substantial efficiency and accuracy.