Date of Original Version
Abstract or Table of Contents
Abstract: "This paper addresses the problem of how to evaluate and optimize the probability of feasible operation for a design that is described by a nonlinear model. This property, which is denoted as the Stochastic Flexibility, represents the cumulative distribution over the feasible region in the space of the uncertain parameters. It is shown that the evaluation problem, which requires a sequence of optimization problems, can be formulated as a single nonlinear programming model which can be extended to design optimization problems for maximizing the stochastic flexibility subject to a cost constraint. A solution method based on Generalized Benders Decomposition is proposed to effectively solve this problem.A comparison with Taguchi's method for minimizing quadratic loss is also presented to point out that the use of a reward function can lead to more sensible designs. Finally, several process design examples are presented to illustrate the determination of trade-offs between cost and flexibility."