A Simulation-Infused Optimization Approach for Decomposing Nonlinear Systems

With the rapid advancements in modern computing technologies, simulation has been increasingly adopted to model complex real-world systems. Yet, such digital computational power remains underutilized in decision-making tasks, mainly due to the difficulties in integrating gray or black box simulation models with algebraic optimization. In this study, we address this by proposing a novel and generic simulation-infused optimization approach. Through decomposition, nonlinear subsystems are surrogated by simulation and approximated by cutting planes in a row generation algorithm for holistic optimization. Exploration and exploitation are introduced to the row generation algorithm via a novel parametric method. The proposed simulation-infused decomposition approach is applied to a multi-depot vehicle routing problem. Proof-of-concept experiments are conducted to validate the performance against benchmarks, showing drastic reductions in computational time and improvements upon conventional optimization methods when real-world nonlinear systems are involved.