The development of new products and processes is one of the most important aspects that industries need to address, in particular during their growth and expansion. This becomes a difficult task due to uncertainties in raw material availabilities, product demand profiles, energy availability, environmental sustainability, and safety. At the early stages of the design, the system is also subject to a myriad of uncertainties, such as estimates of the parameters (recovery rates, process efficiencies, or process reliabilities), because most of the technologies involved are still under development. My Ph.D. work focuses on developing novel algorithms to provide input into decision making on design under uncertainty, for a challenging application, i.e., for designing a life-support-system for survival and well-being of the crew during space missions. The algorithms utilize a simulation-based optimization (SIMOPT) approach that combines a stochastic discrete-event simulation with a deterministic mathematical programming approach to generate multiple, unique realizations of the controlled evolution of the system. The realizations are then analyzed using time-series data-mining techniques and statistical tools to determine the necessary technologies, their deployment schedules (retrofitting) and processing capacities, and the necessary inventory levels to support crew life and activities for the specified mission duration.