Medium-term Capacity Management through Reinforcement Learning - Literature review and concept for an industrial pilot-application

Empty storage shelves and long customer lead times due to a sharp rise in market demand from industries on the one hand (e.g., pharmaceutical products). On the other hand, increasing short-term working or unemployment due to a rapid decline in demand (e.g., automotive). Current supply and demand gaps caused by the COVID-19 pandemic remind us that successful competition in volatile business environments requires rapid adjustments of production capacities. Capacity management (CM) addresses these adjustments by adapting production capacity to market demand. Operations managers of flexible manufacturing systems can adjust the capacity by using various levers (e.g., overtime, used machines, …). To guide these managers, decision support systems (DSS) exist for short-term CM (e.g., shop floor scheduling). However, due to complexity and runtime problems, the decision-making process for medium-term CM is usually carried out with low technical support. Increases in computing power and advances in algorithm performance over the past decades have enabled Machine Learning to solve ever more complex problems such as the aforementioned issues. Reinforcement Learning (RL) in particular has shown good performance in solving short-term CM problems when compared to humans or other established heuristics. In this work we review the current literature for CM using RL in flexible manufacturing systems. We identify an existing lack of knowledge within the overlap of medium-term CM and RL. However, good performance of RL in short-term CM indicates that an application in medium-term CM should be evaluated. In addition, we propose a concept of a method for medium-term CM based on RL to support operations managers in the decision-making process. The resulting DSS could have a significant impact on production performance, especially in terms of capacity adjustment speed.