Graph Neural Networks for Job Shop Scheduling Problems: A Survey

TL;DR

This survey reviews the application of graph neural networks to job shop scheduling problems, highlighting current methods, architectures, and future research directions in this rapidly evolving field.

Contribution

It provides a comprehensive overview of GNN-based methods for JSSPs and FSPs, systematically analyzing their technical components and identifying research gaps.

Findings

GNNs offer promising solutions for JSSPs but face limitations in scalability.

Various GNN architectures are adapted for different scheduling problem types.

Future research can explore more effective GNN models and training strategies.

Abstract

Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the application of graph neural networks (GNNs) to solve JSSPs, albeit lacking a systematic survey of the relevant literature. This paper aims to thoroughly review prevailing GNN methods for different types of JSSPs and the closely related flow-shop scheduling problems (FSPs), especially those leveraging deep reinforcement learning (DRL). We begin by presenting the graph representations of various JSSPs, followed by an introduction to the most commonly used GNN architectures. We then review current GNN-based methods for each problem type, highlighting key technical elements such as graph representations, GNN architectures, GNN tasks, and training algorithms. Finally, we summarize and analyze the advantages and limitations of GNNs in solving JSSPs and provide potential future research opportunities. We hope this survey can motivate and inspire innovative approaches for more powerful GNN-based approaches in tackling JSSPs and other scheduling problems.