Permutation-Invariant and Equivariant Multiagent Reinforcement Learning for Flexible Manufacturing in Industrial IoT
Bibliographic record
Abstract
With the advent of Industrial Internet-of-things (IIoT), flexible manufacturing has gained increasing attention. Continuous changes in market demand, real-time data collection and device interconnectivity have made the production process more dynamic and unpredictable, rendering traditional manufacturing scheduling methods inadequate in addressing these changes. This issue can be framed as a Dynamic Flexible Job Shop Scheduling Problem (DFJSP), which seeks to accommodate ever-evolving production scenarios and requirements by making real-time modifications and optimizing resource allocation. Traditional scheduling algorithms designed for static, single-environment scenarios are increasingly inadequate for handling the growing complexity of production environments. In this context, there is a pressing need for efficient and real-time scheduling algorithms. We propose a Multi-Agent Reinforcement Learning (MARL) algorithm to solve DFJSP, where each device is associated with a corresponding agent, allowing the algorithm to scale flexibly. The complexity and dynamics of scheduling problems introduce additional challenges and complexities in state representation and decision-making. We propose two solutions to alleviate this problem. First, we employ a Heterogeneous Graph Neural Network (HGNN) to capture the relational dependencies between tasks and extract state features. Through multiple feature updates, each agent is enabled to make decisions based on global information. Furthermore, leveraging the inherent Permutation Invariance (PI) and Permutation Equivariance (PE) features of tasks in the waiting queue, we apply hypernetwork techniques to address the issue of dimensionality explosion caused by the excessive state space in scheduling environments. Experiments conducted under various scenario settings demonstrate that our scheduling method can significantly reduce task latency and improve resource utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".