Dual Dynamic Attention Network for Flexible Job Scheduling with Reinforcement Learning

The flexible job shop problem (FJSP) is a classic combinatorial optimization problem that is strongly NP-hard. Recent studies have utilized deep reinforcement learning (DRL) methods for scheduling operations in FJSP problems, achieving results comparable to accurate methods such as OR tools. However, there are still limitations in extracting global representations of machines and operations. This paper proposes the Dual Dynamic Attention Network (DDAN), which addresses these limitations. The proposed method utilizes an in-channel dynamic attention mechanism to capture the global representation of machines and operations. This allows for accurate and efficient representation of complex dependencies between operations and machines, providing effective support for subsequent dispatching model. Assessments using synthetic datasets as well as public benchmarks corroborate the proposed approach’s superiority over traditional priority dispatching rules (PDRs) and state-of-the-art DRL algorithms. In certain cases, it even surpasses deterministic algorithms. Additionally, this method demonstrates superior performance and stronger generalization capabilities compared to current state-of-the-art DRL methods on large-scale FJSP problems that have not been previously encountered.