arXiv cs.AI· Mohammadhossein Ghahramani, Yan Qiao, Mengchu Zhou·· 2 天前AI 评分22
Edge-AI 驱动的学习排序用于循环智能制造中的去中心化任务分配
Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing
AI 导读
研究提出 Edge-AI 驱动的去中心化任务分配框架，在机器端部署轻量决策智能：先以资源感知启发式建立去中心化竞价结构，再用回归式 Edge-AI 学习本地出价近似，最后用紧凑的自编码器正则化成对排序模型对解析出价排序做学习式修正。在高负载、依赖共享资源的离散事件仿真中，该排序方法相比启发式提升了完成任务数、降低了平均拖期与截止期错过率，且差异统计显著，每完成任务平均能耗也下降。
正文 · 原文
Abstract:Task allocation in smart manufacturing systems must operate under decentralized decision-making, dynamic workloads, and shared-resource constraints. In circular manufacturing settings, these challenges are further intensified because tasks compete for reusable, capacity-constrained assets, and machine selection also might affect processing energy. Although learning-based approaches have been explored for task allocation, improvements in predictive modeling do not necessarily translate into better allocation outcomes under decentralized negotiation. This work proposes an Edge-AI-driven decentralized task-allocation framework. We develop lightweight decision intelligence deployed at the machine level. It is developed progressively: first, a resource-aware heuristic establishes the decentralized bidding structure; a regression-based Edge-AI formulation then examines learned local bid approximation, and a compact autoencoder-regularized pairwise ranking model finally provides a learned correction to the analytical bid ordering. Each machine evaluates incoming tasks by using its processing capability, queue state, energy characteristics, and a compact signal representing contention over the reusable shared production asset. The framework is assessed using discrete-event simulation in scenarios characterized by high load and dependence on shared resources. Compared to the heuristic, the proposed ranking method increases completed tasks, reduces average tardiness, and lowers the deadline-miss rate, with statistically significant paired differences. Mean energy per completed task is also reduced. The results indicate that effective learning-assisted allocation depends not only on approximating local decision quantities, but also on shaping the relative preferences that determine negotiation outcomes.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.16433 [cs.LG] |
| (or arXiv:2605.16433v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16433 arXiv-issued DOI via DataCite |
|
| Journal reference: | Under review at IEEE IoT J, 2026 |
Submission history
From: Mohammadhossein Ghahramani [view email]
[v1]
Thu, 14 May 2026 20:38:25 UTC (2,109 KB)
[v2]
Fri, 21 Aug 2026 21:16:38 UTC (5,699 KB)
[v3]
Tue, 29 Sep 2026 11:25:38 UTC (6,368 KB)
[v4]
Tue, 27 Oct 2026 16:43:10 UTC (5,678 KB)
来源：arXiv cs.AI · arxiv.org