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. 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 sim
The increasing sophistication of LLMs combined with the demand for real-time optimization in complex industrial settings is driving this development.
This development indicates a tangible step towards autonomous AI agents directly controlling and optimizing critical industrial operations, moving beyond mere advisory roles.
Open-pit mine scheduling can potentially transition from highly complex, computationally intensive human-assisted processes to more autonomous, real-time optimized, AI-driven systems.
- · Mining companies
- · AI software developers
- · Industrial automation sector
- · Traditional optimization software providers
- · Manual scheduling consultants
Increased efficiency and profitability in open-pit mining operations due to dynamic scheduling.
Expansion of similar autonomous LLM-guided frameworks into other complex industrial logistics and scheduling problems.
Reduced human involvement and potential job displacement in operational planning roles across multiple heavy industries.
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Read at arXiv cs.AI