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
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. This approach overcomes the limitations of traditional MILP methods, offering a scalable and interpretable solution for complex scheduling tasks.
Key Points
- LLM 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
Article Content
From source RSS / original summaryarXiv: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.
This work introduces a simulator-driven Large Language Model (LLM) scheduling framework in which the LLM acts as an autonomous decision-making agent, guided at each step by a custom simulator that encodes geotechnical precedence, extraction-processing coupling, and dynamic capacity constraints directly into the action generation mechanism.
Operating entirely zero-shot within a closed, data-secure environment, the framework produces complete, interpretable extraction and processing schedules without cloud-based inference, domain-specific fine-tuning, or retraining. To provide a trustworthy performance benchmark, a novel MILP formulation is developed that incorporates realistic operational and geotechnical constraints.
Evaluated across mining instances of varying scale and time periods, the LLM-based framework recovers between 94\% and 99\% of the MILP optimal NPV while scaling linearly in computation time. These results position simulator-constrained LLM agents as a practical and scalable alternative to classical optimization for long-horizon industrial scheduling under complex operational constraints.
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