Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

An algorithm that employs the back‐off method to provide optimal solutions for integration of design, control, and scheduling for multiproduct systems is presented, featuring a flexibility and feasibility analysis. The algorithm employs Monte Carlo (MC) sampling to generate a large number of random realizations, and simulate the system to determine feasibility. Back‐off terms are determined and incorporated into a new flexibility analysis to approximate the effect of stochastic uncertainty and disturbances. Through successive iterations, the algorithm converges, terminating on a solution that is robust to a specified level of process variability due to stochastic realizations in the disturbances and uncertain parameters. The proposed algorithm has been successfully applied to a multiproduct continuous stirred tank reactor for which optimal design, control, and scheduling decisions are identified, subject to stochastic uncertainty and disturbance. The present approach has been compared to a critical‐set (multiscenario) method showing the benefits and limitations of both approaches.