Abstract: An update to an encoder is implemented utilizing information regarding performance of a reinforcement learning (RL) agent. This allows the emphasis to be placed not only on improving the performance of the RL agent, but on providing that the data within the encoding is both required and in such a form that it is optimal for the RL agent to learn, thereby reducing complexity and increasing speed of learning.

Abstract: A procedure to train an online scheduling system using Reinforcement Learning agents to process any kind of product variant and any kind of machine configuration is disclosed. The novel approach of scheduling jobs or products in a flexible manufacturing system is to train Deep Reinforcement Learning agents with generated training data. One agent may represent a product and may autonomously guide the product through the manufacturing system, including decisions regarding resource allocations (which module should process which operation) and transport decisions. Dependent on the mode to be trained, the identical job-specification for same, job-specifications from the same cluster for similar, and job-specifications from different clusters for different are chosen. This solution may handle any product variant to be produced within the considered system.

Abstract: The method for self-learning manufacturing scheduling for a flexible manufacturing system (FMS) with processing entities that are interconnected through handling entities is disclosed. The manufacturing scheduling is learned by a reinforcement learning system on a model of the flexible manufacturing system. The model represents at least the behavior and the decision making of the flexible manufacturing system, and the model is transformed in a state matrix to simulate the state of the flexible manufacturing system. A self-learning system for online scheduling and resource allocation is also provided. The system is trained in a simulation and learns the best decision from a defined set of actions for many every situation within an FMS. A decision may be made in near real-time during a production process and the system finds the optimal way through the FMS for every product using different optimization goals.