arXiv AI By Jonathan Hoss, Moritz Link, Noah Klarmann

Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

Read the original on arXiv AI →

arXiv:2604. 23841v2 Announce Type: replace-cross Abstract: Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 17

Variational Approach for Job Shop Scheduling

The paper introduces Variational Graph-to-Scheduler (VG2S), a framework that applies variational inference to the Job Shop Scheduling Problem (JSSP). By decoupling representation learning from policy optimization using a variational graph encoder and an ELBO-based objective, VG2S improves training stability and robustness to hyperparameter changes. Experiments show that VG2S outperforms state‑of‑the‑art deep reinforcement learning baselines and traditional dispatching rules, especially on large‑scale benchmark instances such as DMU and SWV.

By Seung Heon Oh, Jiwon Baek, Hyunjin Oh, Kiyoung Cho, Heechang Yoon, Jong Hun Woo
arXiv Machine Learning
Sep 23

Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing

The paper introduces a novel end‑to‑end, size‑agnostic graph reinforcement learning framework for the one‑dimensional bin packing problem (1D‑BPP). It models packing as a Markov decision process on an item‑compatibility graph, where a graph neural network actor‑critic policy learns to merge compatible partial bins. Empirical results on the BPPLIB benchmark show that the learned policy reduces the mean optimality gap of a constructive heuristic from 2.66 % to 2.31 %, performs competitively against other learned methods, and outperforms a state‑of‑the‑art learned solver on the hardest benchmark family.

By M. Asl{\i} Ayd{\i}n
arXiv AI
Jun 16

A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling

arXiv:2603. 23249v2 Announce Type: replace-cross Abstract: Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computation graphs consist of dependent tasks competing for limited heterogeneous resource pools.

By Ruisong Zhou, Haijun Zou, Li Zhou, Chumin Sun, Zaiwen Wen
arXiv AI
Aug 18

ClawGym II: Exploring Black-Box RL on Agent Harness

arXiv:2608. 16798v1 Announce Type: cross Abstract: Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment.

By Huatong Song, Fei Bai, Ming Yang, Renyuan Li, Jia Deng, Jujie He, Zhange Zhang, Daixuan Cheng, Yan Xing, Qi Yun, Xuxing Chen, Danyang Li, Feng Chang, Chuan Hao, Ran Tao, Jian Yang, Bryan Dai, Wayne Xin Zhao, Mingjie Tang, Ji-Rong Wen