Multi-Agent Continuous Decision-Making for the Continuous Dynamic Flexible Job Shop Scheduling Problem

The Continuous Dynamic Flexible Job Shop Scheduling Problem (C-DFJSP) represents a critical challenge in smart manufacturing due to its continuous, dynamic nature, requiring real-time decision-making and high adaptability to diverse factory configurations. We provide a solution to the C-DFJSP problem, where the notion of a global optimum is inherently ill-defined. To solve the C-DFJSP, we propose a Multi-Agent Continuous Decision-Making (MACD) system. MACD leverages a novel state and observation design to extract novel factory-invariant features, enabling a learned scheduling policy to generalise across different factories, machine capabilities, and product libraries. MACD is an enhanced multi-agent continuous scheduling system that integrates Graph Neural Networks (GNNs) and Reinforcement Learning (RL) within a multi-agent framework. In addition, MACD employs an innovative negotiation strategy – a hybrid method combining Shortest Processing Time (SPT) and Least Loaded Machine (LLM) heuristics – supported by an empirical study to resolve rare conflicts among machine agents, enhancing adaptability and performance. Extensive experiments on public datasets validate MACD’s superior ability to generalise across diverse factory configurations, allowing a trained scheduling policy to be directly used in different factories with varying machine flexibilities, utilisation levels, and product libraries.