Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2024

Abstract

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at each construction step. This paper proposes a novel DRL-guided improvement heuristic for solving JSSP, where graph representation is employed to encode complete solutions. We design a Graph-Neural-Network-based representation scheme, consisting of two modules to effectively capture the information of dynamic topology and different types of nodes in graphs encountered during the improvement process. To speed up solution evaluation during improvement, we present a novel message-passing mechanism that can evaluate multiple solutions simultaneously. We prove that the computational complexity of our method scales linearly with problem size. Experiments on classic benchmarks show that the improvement policy learned by our method outperforms state-of-the-art DRL-based methods by a large margin.

Keywords

Deep Reinforcement Learning, Graph Neural Network, Job Shop Scheduling, Combinatorial Optimization

Discipline

Graphics and Human Computer Interfaces | OS and Networks

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 12th International Conference on Learning Representations, Vienna, Austria, 2024 May 7-11

First Page

1

Last Page

21

Publisher

ICLR

City or Country

Vienna, Austria

Copyright Owner and License

Authors

Additional URL

https://openreview.net/forum?id=jsWCmrsHHs

Download

Share

COinS