#deep-reinforcement-learning2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago3

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Learning Flexible Job Shop Scheduling under Limited Buffers and Material Kitting Constraints

Researchers developed a deep reinforcement learning approach using heterogeneous graph networks to solve Flexible Job Shop Scheduling Problems with limited buffers and material kitting constraints. The method outperforms traditional heuristics by improving buffer utilization and decision quality through better modeling of complex dependencies in production scheduling.

AIBullisharXiv – CS AI · 4h ago3

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Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing

Researchers developed LACE-RL, a deep reinforcement learning framework that optimizes serverless computing by balancing cold-start latency and carbon emissions. The system dynamically adjusts keep-alive durations based on real-time carbon intensity and workload patterns, achieving 51.69% fewer cold starts and 77.08% lower idle carbon emissions compared to static policies.