APEX: An Extensible Model for Agent-Assisted Production Scheduling

arXiv:2609.33430 · cs.AI, cs.NE · Submitted 2026-09-27 · Read on arXiv

cs.AI, cs.NE

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• APEX — 34× in this paper · explained in 1 episode
• Benchmark — 22× in this paper · explained in 2 episodes
• Core — 21× in this paper · explained in 2 episodes
• Digital Twin — 6× in this paper · explained in 5 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Actor — 4× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Admissibility — 2× in this paper · explained in 2 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems
• PyJobShop: Solving scheduling problems with constraint programming in Python

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