A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer

This work deals with a scheduling problem of a real-world production process in the metal–working industry. The production process can be described as an offline stochastic flexible flow-shop problem with limited buffers. In a first step, we analyze a simplified model and develop a variable neighborhood search based solution approach where we use multiple scenarios to evaluate the objective. Second, the solution approach is adapted to a real-world case using a detailed discrete-event simulation to evaluate the production plans. We are able to improve state-of-the-art production plans statistically significant by 3–10%.