Predicting performance indicators with ANNs for AI-based online scheduling in dynamically interconnected assembly systems

Abstract

Mass customization demands shorter manufacturing system response times due to frequent product changes. This increase in system dynamics imposes additional flexibility requirements especially on assembly processes, as complexity accumulates in this last step of value creation. Flexible and dynamically interconnected assembly systems can meet the increased requirements as opposed to traditional dedicated assembly line approaches. The high complexity and dynamical environment in these kinds of systems lead to the demand for real-time online control and scheduling solutions. Within the decision-making of online scheduling, the capability of predicting the consequences of available actions is crucial. In real-time environments, running extensive discrete-event simulations to evaluate how actions unfold requires too much computing time. Artificial neural networks (ANN) are a viable alternative to quickly evaluate the potential future performance value of a production state for online production planning and control. They can predict performance indicators such as the expected makespan given the current production status. Leveraging recent advances in artificial intelligence (AI) game algorithms, an assembly control system based on Google DeepMind’s AlphaZero was created. Specifically, an ANN is incorporated into the approach that suggests favorable job routing decisions and predicts the value of actions. The results show that the trained network can predict favorable actions with an accuracy of over 95% and estimate the makespan with an error smaller than 3%.