Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
The rise of smart manufacturing under Industry 4.0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible job-shop scheduling problem (FJSP) has attracted significant attention due to its complex constraints and strong alignment with real-world production scenarios. Current deep reinforcement learning (DRL)-based approaches to FJSP predominantly employ constructive methods. While effective, they often fall short of reaching (near-)optimal solutions. In contrast, improvement-based methods iteratively explore the neighborhood of initial solutions and are more effective in approaching optimality. However, the flexible ma...
| Authors | Jiaqi Wang, Zhiguang Cao, Peng Zhao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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