Designers should try to design systems that are resilient to adverse conditions during a system’s lifetime. The resilience of a system under time-dependent adverse conditions can be assessed by modeling the degradation and recovery of the system’s components. Decision makers in a firm should attempt to find the optimal design to make the system resilient to the various adverse conditions. A risk-neutral firm maximizes the expected profit gained from fielding the system, but a risk-averse firm may sacrifice some profit in order to avoid failure from these adverse conditions. The uniqueness of this paper lies in its model of a design firm’s risk aversion with a utility function or Value-at-Risk (VAR) and its use of that model to identify the optimal resilient design for the risk-averse firm. These risk-averse decision-making methods are applied to a design firm determining the resilience of a new engineered system. This paper significantly advances the engineering design discipline by modeling the firm’s appetite for risk within the context of designing a system that can fail due to degradation in the presence of adverse events and can respond to and recover from failure. Since the optimization model requires a complex Monte Carlo simulation to evaluate the objective function, we use a ranking and selection method and Bayesian optimization to find the optimal design. This paper incorporates the design of the wind turbine and the reliability and restoration of the turbine’s components for both risk-neutral and risk-averse decision makers. The results show that in order to make the system more resilient, risk-averse firms should pay a larger design cost to prevent catastrophic costs of failure. In this case, the system is less likely to fail due to the high resilience of its physical components.

Designing complex systems with many parameters requires computationally expensive simulations and selecting both discrete and continuous design parameters. How to optimize design when future conditions may change, or when system is used differently than expected?

Flexibility in engineering system design: •Flexibility in system design and implications for aerospace systems (Saleh et.al 2003) •A flexible and robust approach for preliminary engineering design based on designer's preference (Nahmet.al, 2007) •A real options approach to hybrid electric vehicle architecture design for flexibility (Kang et.al 2016) •Our research: •Simulation optimization •Long range uncertainty •Add flexibility and robustness to design

Research Problem •Determine how resilience should be integrated into a firm’s design decisions •Optimize design for a risk-averse firm that incorporates resilience