Performance of MRP, RPS, and ConWIP in Stochastic Multi-Stage Systems: An Item-Level Simheuristic Study

This study conducted a comprehensive comparison of three production planning and control systems – MRP, RPS, and ConWIP – across 18 stochastic multi-item, multi-stage production environments. The evaluation incorporated varying production system structures (flow, job, hybrid shops), planned utilization levels, and customer-required lead-time tightness. A simheuristic combining Simulated Annealing, Simulation Budget Management, and Exponential Range Reduction was applied to tune planning parameters at the item and component level, significantly enlarging the search space compared to earlier, system-level studies. The simheuristic consistently outperformed the baseline, with gains most pronounced under high utilization and complex structures. ConWIP proved most robust in medium- to high-load environments by stabilizing work-in-process. In contrast, MRP performed best under low utilization and relaxed due-dates, as demand-driven logic is most effective when congestion is minimal. While RPS was the least flexible and benefited least from tuning, it offered the highest efficiency with minimal computational overhead, providing a clear trade-off between optimization depth and responsiveness.