Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2023

Abstract

Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be scheduled on one out of multiple machines and is often required in practice. Such one-to-many relationship brings additional complexity in both decision making and state representation. This article considers the well-known flexible job-shop scheduling problem and addresses these issues by proposing a novel DRL method to learn high-quality PDRs end to end. The operation selection and the machine assignment are combined as a composite decision. Moreover, based on a novel heterogeneous graph representation of scheduling states, a heterogeneous-graph-neural-network-based architecture is proposed to capture complex relationships among operations and machines. Experiments show that the proposed method outperforms traditional PDRs and is computationally efficient, even on instances of larger scales and different properties unseen in training.

Keywords

Flexible job-shop scheduling, Graph neural network, Deep reinforcement learning

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

IEEE Transactions on Industrial Informatics

Volume

19

Issue

2

First Page

1600

Last Page

1610

ISSN

1551-3203

Identifier

10.1109/TII.2022.3189725

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

http://doi.org/10.1109/TII.2022.3189725

Download

Share

COinS