arXiv AI
SLAB: Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories
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Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories
arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.
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Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation
The study evaluates six deep reinforcement learning (DRL) approaches for high‑performance computing (HPC) job scheduling, comparing Proximal Policy Optimization (PPO), Advantage Actor‑Critic (A2C), and Deep Q‑Network (DQN) with and without action masking. Across two real Slurm workload traces—one homogeneous and one heterogeneous—masked DRL treatments complete evaluation, while unmasked ones are truncated. On the heterogeneous trace, MaskablePPO outperforms baseline heuristics in all three metrics, achieving waiting times within 10% of the best heuristics and a 22% lower maximum bounded slowdown than Last‑Come, First‑Served (LCFS).
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StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
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arXiv AI
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