Permutation-Invariant and Equivariant Multiagent Reinforcement Learning for Flexible Manufacturing in Industrial IoT

With the advent of Industrial Internet of Things (IIoT), flexible manufacturing has gained increasing attention. Continuous changes in market demand, real-time data collection and device interconnectivity have made the production process more dynamic and unpredictable, rendering traditional manufacturing scheduling methods inadequate in addressing these changes. This issue can be framed as a dynamic flexible job-shop scheduling problem (DFJSP), which seeks to accommodate ever-evolving production scenarios and requirements by making real-time modifications and optimizing resource allocation. Traditional scheduling algorithms designed for static, single-environment scenarios are increasingly inadequate for handling the growing complexity of production environments. In this context, there is a pressing need for efficient and real-time scheduling algorithms. We propose a multiagent reinforcement learning (MARL) algorithm to solve DFJSP, where each device is associated with a corresponding agent, allowing the algorithm to scale flexibly. The complexity and dynamics of scheduling problems introduce additional challenges and complexities in state representation and decision-making. We propose two solutions to alleviate this problem. First, we employ a heterogeneous graph neural network (HGNN) to capture the relational dependencies between tasks and extract state features. Through multiple feature updates, each agent is enabled to make decisions based on global information. Furthermore, leveraging the inherent permutation invariance (PI) and permutation equivariance (PE) features of tasks in the waiting queue, we apply hypernetwork techniques to address the issue of dimensionality explosion caused by the excessive state space in scheduling environments. Experiments conducted under various scenario settings demonstrate that our scheduling method can significantly reduce task latency and improve resource utilization.