Deep Reinforcement Learning Using Multiview Graph Attention Mechanisms for Flexible Job Shop Scheduling

Bidirectional job-shop scheduling with graph-theoretic features: a graph attention network and proximal policy optimization approach

A novel DRL-based approach that integrates bidirectional scheduling with graph-theoretic features to effectively solve JSSP is introduced, which achieves superior performance compared to traditional heuristics and classic DRL methods, while maintaining competitive results against recent state-of-the-art approaches.

Deep Reinforcement Learning for Flexible Job Shop with Multi-AGV Production Systems via Heterogeneous Graph Neural Networks

Flexible job shop scheduling with multiple automated guided vehicles (FJSP-AGV) is a challenging production scheduling problem in intelligent manufacturing, where operation sequencing, machine assignment, AGV allocation, and transportation decisions are tightly coupled. Existing exact and meta-heuristic methods can obtain high-quality solutions, but they usually require considerable computational time for large-scale instances. Meanwhile, conventional dispatching rules can make fast decisions but often fail to capture the complex interactions among operations, machines, and AGVs. To address these challenges, this paper proposes an end-to-end deep reinforcement learning framework based on heterogeneous graph neural networks for solving FJSP-AGV. Specifically, a heterogeneous graph is constructed to represent the scheduling state, where operations, machines, and AGVs are modeled as different types of nodes, and their relationships are described by operation–machine and operation–AGV arcs. Based on this representation, a heterogeneous graph neural network is developed to extract scheduling information from different production resources. In particular, a meta-path aggregation mechanism is introduced to capture the complex interaction patterns among operations, machines, and AGVs. The proximal policy optimization algorithm is then employed to train the scheduling policy in an end-to-end manner. Experimental results on public benchmark instances and real-world cases demonstrate that the proposed method outperforms composite heuristic rules and achieves a favorable balance between solution quality and computational efficiency compared with existing state-of-the-art methods. These results indicate that the proposed HGNN-DRL framework is effective for fast and intelligent scheduling decision-making in FJSP-AGV environments.

A low-node-density disjunctive graph model for flexible job shop scheduling

This study proposes a structurally simple, low-node-density disjunctive graph model, then performs feature extraction using Graph Neural Networks (GNNs), and finally optimize convergence using the Migrating Birds Optimization—an intelligent optimization algorithm proven effective for scheduling decision problems.

Energy-efficient flexible job shop scheduling with heterogeneous graph embedding and adaptive proximal policy optimization

Preference-Guided Multi-Policy Optimization for Flexible Job Shop Scheduling

PGMPO is proposed, a novel learning framework consisting of a simple but effective multi-policy modeling approach that allows a single network to represent multiple decision-makers, and a preference-driven model optimization method that effectively guides policies to learn diverse and specialized problem-solving strategies without the need for explicit reward functions.

A Deepsets-Guided Framework for Learning Job Priorities In Single-Machine Scheduling

Scheduling is a fundamental decision-making problem in chemical engineering as well as numerous other sectors, arising in manufacturing, energy systems, and supply chains. Many scheduling problems are NP-hard [1], meaning that even small problems are computationally hard to solve deterministically. As a result, existing exact and heuristic methods face trade-offs between scalability, solution quality, and generalizability. This work addresses these limitations through a hybrid machine learning–optimization framework for the single-machine total tardiness scheduling problem (SMTTP). We introduce the DeepSets-Guided Scheduling Framework (DGSF), a hybrid methodology that integrates data-driven priority learning with structured optimization for single-machine scheduling. First, we propose a geometric instance classification rule that characterizes scheduling instances through aggregate structural parameters, enabling models trained on small instances to generalize to larger instances within the same structural class. Second, we develop a modified DeepSets [2] machine learning (ML) architecture that processes variable-sized sets of jobs and produces job-aligned priority scores. The model combines job-level feature transformations with an attention-based aggregation mechanism to incorporate instance-level context, allowing priority estimation to depend jointly on job and instance characteristics. Input features are constructed to retain interpretability and include normalized processing, release and due times, slack-based measures, and features derived from classical heuristics. Third, we introduce a two-stage post-processing step. A fast local pairwise-swap heuristic improves the predicted sequence, which is then used to warm-start a neighborhood-restricted continuous-time mixed-integer programming formulation. By explicitly limiting the search space around the learned solution, this formulation achieves high-quality refinement while controlling combinatorial complexity. Computational experiments indicate that the learned priority structure aligns with classical one-shot dispatching heuristics while improving solution quality. On instances with up to 120 jobs, DGSF consistently outperforms these heuristics, achieving optimality gaps of 3–6% compared to 26–31% for the best-performing one-shot heuristic. Furthermore, DGSF maintains high-quality solutions across different instance structures, time discretizations, and product catalogues.