Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling
Quick Answer
Sim2Schedule introduces a simulator-guided LLM framework for autonomous open-pit mine scheduling, achieving 94%-99% of MILP optimal NPV while operating in a zero-shot environment.
Quick Take
This approach overcomes the limitations of traditional MILP methods, offering a scalable and interpretable solution for complex scheduling tasks.
Key Points
- framework operates without cloud inference or domain-specific fine-tuning.
- Achieves linear scaling in computation time across various mining instances.
- Develops a novel MILP formulation for realistic operational constraints.
- Provides interpretable extraction and processing schedules.
- Positions LLM agents as viable alternatives to classical optimization methods.
Paper Resources
Source Excerpt
arXiv:2606. 10286v1 Announce Type: new Abstract: Open-pit mine scheduling is a critical process for maximizing economic return under complex geotechnical and operational constraints. While Mixed-Integer Linear Programming (MILP) provides mathematically optimal baselines, its exponential computational complexity and inability to adapt in real time limit its practical deployment in dynamic industrial environments. …
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
AINTMA, an autonomous test management architecture utilizing six specialized AI agents, achieves 88.4% test prioritization accuracy and reduces defect escape rates from 8.3% to 2.1%. The system demonstrates a 340% ROI within nine months, showcasing the potential of agentic AI in enhancing software quality management in cloud environments.