Simulation-based Optimization of Material Requirements Planning Parameters

The performance of modern production systems often depends upon automated production planning strategies such as material requirements planning. Parametrizing, evaluating and comparing these strategies by testing them in the real world is often difficult and prohibitively resource intensive. State-of-the-art computer simulation can be used to adequately model the production processes and predict the relevant performance metrics without investing valuable production capacities. Heuristic optimization procedures can build on these simulations to fine-tune production planning strategies. A major obstacle for this simulation-based optimization approach, however, lies in its computational requirements since accurate production simulations require their fair share of computation time. In this work, we will demonstrate the use of heuristic optimization to learn optimal production strategies for a bi-objective high-dimensional real world scenario and explore how to reduce the computational cost of the heuristic search by use of surrogates and dimensionality reduction. Results indicate that the employed approach achieved solutions that could outperform the production planning parameters currently used in the real world.