InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
arXiv:2605.00369v4 Announce Type: replace Abstract: We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based evolutionary search, such as AlphaEvolve, which demonstrates strong performance on static and highly structured problems such as mathematical discovery, but is not directly suited to dynamic inventory settings with online updates. We propose InvEvolve, an end-to-end inventory policy evolution and inference framework grounded in confidence-interval-based certification
Advances in LLM capabilities are enabling their application to increasingly complex and dynamic real-world problems, moving beyond static environments.
This development indicates a tangible path for AI, specifically LLMs, to automate and optimize critical supply chain and inventory management functions, offering significant efficiency gains.
LLMs can now generate and evolve inventory policies with performance guarantees in non-stationary online settings, rather than being confined to static or structured problems.
- · Supply chain management software companies
- · Logistics and retail sectors
- · AI/ML developers
- · Traditional inventory policy consultants
- · Companies with suboptimal inventory management
Automated inventory management will reduce waste and optimize stock levels across various industries.
This efficiency gain could lead to lower operational costs and improved profit margins for businesses adopting such AI policies.
The widespread deployment of such powerful AI agents could reshape employment needs in supply chain planning and logistics.
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Read at arXiv cs.LG