Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling

TL;DR

This paper introduces MIStar, a memory-enhanced improvement heuristic framework with a novel graph representation and neural network for flexible job shop scheduling, achieving superior solutions efficiently.

Contribution

It proposes a novel heterogeneous disjunctive graph and a memory-enhanced graph neural network to improve solution quality in flexible job shop scheduling.

Findings

MIStar outperforms traditional heuristics and DRL methods.

The approach achieves higher-quality solutions with fewer iterations.

Experimental results validate the effectiveness of the proposed framework.

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 heterogeneous disjunctive graph that explicitly models the operation sequences on machines to accurately represent scheduling solutions. Moreover, a memoryenhanced heterogeneous graph neural network (MHGNN) is designed for feature extraction, leveraging historical trajectories to enhance the decision-making capability of the policy network. Finally, a parallel greedy search strategy is adopted to explore the solution space, enabling superior solutions with fewer iterations. Extensive experiments on synthetic data and public benchmarks demonstrate that MIStar significantly outperforms both traditional handcrafted improvement heuristics and state-of-the-art DRL-based constructive methods.