In design and operation of complex processing systems, two categories of uncertainty play an important role : uncertainty with respect to the realization of continuous parameters uncertainty with respect to discrete states Novel analytical tools (stochastic flexibility index, expected stochastic flexibility index) to simultaneously account for both type of uncertainties in process design and evaluation have recently appeared in the literature. However, discrete-state uncertainty introduces new operating states, the number of which grows exponentially. This computationally prohibits the direct evaluation of stochastic measures for the simultaneous quantification of flexibility and reliability, especially for large systems. This work focuses on developing automatic procedures for reducing the number of operating states which must be evaluated in complex process systems. First, based on structural flowsheet information and by the use of minimal cut sets theory, the relative reliability importance of the different units and subsystems is assessed, resulting in the construction of a reduced fault event tree. Rules are devised with which reliability bottlenecks are properly identified. An efficient numerical procedure has been implemented for the estimation of bounds for the expected stochastic flexibility index.