A Reinforcement Learning Approach for Supply Chain Management

Abstract. The paper presents a decentralized supply chain management approach based on reinforcement learning. Our supply chain scenario consists of loosely coupled yield optimizing scheduling agents trying to learn an optimal acceptance strategy for the offered jobs. The optimizer constructs a mandatory schedule by inserting the requested jobs, which arrive stochastically from the customers, gradually into a production queue if the job yields a sufficient return. To reduce complexity the agents are divided into three components. A supply chain interface, classifying job offers, a reinforcement learning algorithm component, which makes the acceptance decision and a deterministic scheduling component, which processes the jobs and generates a preliminary state space compression. The reinforcement learning algorithm accepts offers according to their delivery due date, the job price, the timeout penalty cost, and the information provided by the scheduling component. The tasks are finally executed on the suppliers machine following the queue’s schedule. In a performance comparison of our yield optimizing agent it turns out that the reinforcement learning solution outperforms the simple acceptance heuristic for all training states