[2603.02846] Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling

Computer Science > Machine Learning arXiv:2603.02846 (cs) [Submitted on 3 Mar 2026] Title:Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling Authors:Jiaqi Wang, Zhiguang Cao, Peng Zhao, Rui Cao, Yubin Xiao, Yuan Jiang, You Zhou View a PDF of the paper titled Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling, by Jiaqi Wang and 6 other authors View PDF HTML (experimental) Abstract: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 machine allocation in FJSP poses significant challenges to the application of this framework, including accurate state representation, effective policy learning, and efficient search strategies. To address these challenges, this paper proposes a Memory-enhanced Improvement Search framework with heterogeneous graph representation--MIStar. It employs a novel ...