A first attempt to enhance Demand-Driven Material Requirements Planning through reinforcement learning

Abstract

Production planning methods, which are meant to schedule efficiently production orders to meet costumer demands, offer the possibility to fix some parameters to better fit to a specific industrial context. These parameters can be fixed either according to the user's prior knowledge, or according to decision support tools. Optimization algorithms, which require prior knowledge of costumer demand, are most-commonly used for decision-support. Fewer decision-support tools based on reinforcement learning and requiring no prior knowledge of costumer demand have also been suggested in recent years to parameterize most common production planning systems. This paper investigates such a reinforcement-learning approach to parameterize Demand-Driven Materials Requirements Planning (DDMRP), which is a recent production planning method proven to be successful to avoid stockouts while minimizing the on-hand inventory. Despite approximate and exact optimization methods have been suggested in literature to parameterize DDMRP, a reinforcement learning approach remains to be investigated. We suggest a SARSA (State–action–reward–state–action) algorithm to the problem and test it on a randomly-generated instance. The first results are promising in regards to the use of a reinforcement learning approach for the dynamic parameterization of the DDMRP.