Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning

TL;DR

This paper introduces a dual-attention network combined with reinforcement learning to improve flexible job shop scheduling, achieving higher solution quality and better generalization than existing methods.

Contribution

The paper proposes a novel dual-attention network framework integrated with reinforcement learning for FJSP, enhancing feature extraction and decision-making capabilities.

Findings

Outperforms traditional priority dispatching rules and state-of-the-art DRL methods.

Achieves results comparable to exact optimization methods in some cases.

Demonstrates strong generalization to large-scale and real-world FJSP tasks.

Abstract

Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decisionmaking. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks.