Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning (2106.01086v1)

Abstract: We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSSP as a sequential decision-making problem with graph representation of the state to consider the structure of JSSP. In solving the formulated problem, the proposed framework employs a GNN to learn that node features that embed the spatial structure of the JSSP represented as a graph (representation learning) and derive the optimum scheduling policy that maps the embedded node features to the best scheduling action (policy learning). We employ Proximal Policy Optimization (PPO) based RL strategy to train these two modules in an end-to-end fashion. We empirically demonstrate that the GNN scheduler, due to its superb generalization capability, outperforms practically favored dispatching rules and RL-based schedulers on various benchmark JSSP. We also confirmed that the proposed framework learns a transferable scheduling policy that can be employed to schedule a completely new JSSP (in terms of size and parameters) without further training.

• The paper introduces a GNN-RL framework that formulates job-shop scheduling as a sequential decision-making process.
• It employs graph neural networks for state representation and uses PPO-based reinforcement learning for robust, transferable policy learning.
• Empirical results demonstrate significant makespan reductions versus traditional priority dispatching rules and other RL approaches.

Learning to Schedule Job-Shop Problems Using Graph Neural Networks and Reinforcement Learning

The paper titled "Learning to schedule job-shop problems: Representation and policy-learning using graph neural network and reinforcement learning" presents a novel framework for addressing the complex issue of job-shop scheduling problems (JSSP) using a combination of graph neural networks (GNNs) and reinforcement learning (RL). The authors present a methodological advancement by formulating JSSP as a sequential decision-making process using a graph-based representation of the problem. This approach is distinctive as it leverages GNNs for encapsulating the spatial structure of JSSP as graph representations and utilizes RL to optimize the scheduling policy derived from these representations.

Methodological Framework

The paper outlines a framework where the state of the JSSP is represented as a graph, capturing the complex interactions and constraints within the problem. The GNNs are employed to learn node features, effectively embedding the state information into a continuous vector space. This embedding process is critical as it serves as the foundation for the policy learning phase where the task is to determine the optimal scheduling actions. The GNNs demonstrate robust generalization by successfully encapsulating operation sequences and machine-job interactions, which are essential in JSSP.

For the RL component, the authors adopt Proximal Policy Optimization (PPO), an advanced RL algorithm known for its stability and reliability in training neural networks on control tasks. The PPO framework facilitates end-to-end training of both representation (via GNNs) and policy learning modules, optimizing the decision-making process over large state and action spaces effectively. Notably, the RL policy learns to transcend problem-specific nuances, enabling transferability across different JSSP configurations, including those not encountered during the model training phase.

Empirical Evaluation

The empirical evaluation is comprehensive, with the proposed GNN-based scheduling framework tested against established priority dispatching rules (PDRs) and RL-based schedulers. Results indicate that the GNN scheduler consistently outperforms conventional PDRs in various benchmark settings, demonstrating significant reductions in the makespan. In particular, the GNN scheduler exhibits strong performance across a range of test conditions, including unseen JSSP instances and different scales of problem sizes, underscoring its adaptability and robustness.

The comparative analysis against learning-based methods such as Multi-Agent RL and Multi-Class DQN showcases the superiority of the proposed approach. The GNN scheduler delivers superior scheduling accuracy and efficiency without the need for extensive retraining, a notable strength in scalability and computational feasibility for industrial applications.

Practical and Theoretical Implications

The ability of the GNN scheduler to generalize beyond training distribution highlights its potential application in dynamic and large-scale manufacturing environments. The insights offered by this paper pave the way for integrating advanced graph-based learning techniques with RL in operational research and optimization domains. The formulation also opens up avenues for investigating the incorporation of additional constraints and dynamic environments within the scheduling framework, addressing real-world complexities ubiquitously present in manufacturing and production systems.

Conclusion

The approach established in this paper signifies a strategic intersection of GNNs and RL to tactically address the nuances of JSSP. Future work may delve into optimizing the computational efficiency of GNN operations further or extend the model to accommodate more complex scheduling environments such as flow shops or parallel machine setups. This paper effectively places itself as a significant contribution to advancing automated scheduling solutions through AI, with promising implications for both theory and practice in artificial intelligence and operations management.