Abstract
Production scheduling in the process industry is often treated as a static optimization problem, although real plants require frequent rescheduling due to disturbances such as rush orders, equipment breakdowns, and changes in processing times. This paper compares a simulation-optimization approach that couples a discrete-event simulator with an evolutionary algorithm (EA) with a sequence-based mixed-integer linear programming (MILP) formulation for real-time scheduling of multistage batch systems. Both methods are embedded in an event-driven rolling-horizon framework under strict computation time limits.In static experiments for a 3-stage, 2-machine flow-shop setting (10 products, 20 orders, random processing times), the EA achieved lower makespans across all tested time budgets, improving results by about 7-13% on average compared to the MILP approach. In real-time experiments (40 initial orders, maintenance, three rush orders, 10 s and 60 s periodic updates), the solution quality of the MILP approach was lower after disturbances under restricted computation times. Each experiment was repeated 25 times with identical randomly generated processing times across methods per run; in 4% of all runs no solution was obtained within the available response time. Including explicit allocation decisions for the EA increases flexibility but can reduce short-term responsiveness within the rolling-horizon setting.Overall, the results indicate that MILP can be competitive in stable scenarios when sufficient solver time is available, whereas simulation-optimization is better suited to reactive scheduling in real-time settings where rapid schedule adaptation is critical.