Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2025

Abstract

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods fail to learn relationships between FJSP nodes, such as interactions between operations of different jobs, leading to limited interpretability and performance. To address these issues, we propose a dual operation aggregation graph neural network (GNN) for solving FJSP. Specifically, we decouple the disjunctive graph into two distinct graphs, reducing graph density and clarifying relationships between machines and operations, thus enabling more effective aggregation and understanding by neural networks. We develop two distinct graph aggregation methods to minimize the influence of non-critical machine and operation nodes on decision-making while enhancing the model's ability to account for long-term benefits. Additionally, to achieve more accurate multi-objective estimation and mitigate reward sparsity, we design a reward function that simultaneously considers machine efficiency, schedule balance, and makespan minimization. Extensive experimental results on well-known datasets demonstrate that our model outperforms state-of-the-art models and exhibits excellent generalization capabilities, effectively addressing the challenges of cloud manufacturing.

Keywords

Combinatorial Optimization, Flexible Job-Shop Scheduling Problem, Graph Neural Network, Reinforcement Learning, flexible job-shop scheduling problem, cloud manufacturing, graph neural networks, reinforcement learning, operation aggregation, Industry 4.0, scheduling optimization, multi-objective reward, generalization, disjunctive graph modeling

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2025 ACM Web Conference (WWW’25)

First Page

4089

Last Page

4100

ISBN

9798400712746

Identifier

10.1145/3696410.3714616

Publisher

Association for Computing Machinery, Inc

City or Country

Sydney

Additional URL

http://doi.org/10.1145%2F3696410.3714616

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