ABSTRACT
ABST R AC T This paper introduces a novel algorithm utilizing simulationbased optimization approach to provide insight to early-stage design decisions for systems with high recycle rates and uncertain process performances. The algorithm combines a stochastic discrete-event simulation with a deterministic mathematical programming approach to generate multiple, unique realizations of controlled evolution of a system. The realizations are then analyzed to determine necessary technologies, their retrot plan, and necessary inventory levels. The process synthesis and design optimization of a life-support-system for survival and well-being of the crew during a space mission is discussed as an application.
