AI Insight
This study presents InvEvolve, a framework that uses large language models to generate inventory management policies for businesses facing changing demand patterns. The system processes demand data along with contextual information to create interpretable policies with statistical safety guarantees, addressing limitations of existing AI approaches that work well only on static problems. Testing on both synthetic and real-world retail data shows InvEvolve outperforms traditional inventory policies and deep learning methods while providing theoretical performance bounds.
Why it matters
The research offers a practical tool for retail and supply chain management that combines the flexibility of AI with mathematical guarantees of safety and performance. This addresses a critical gap in inventory optimization where businesses need both adaptability to changing conditions and reliability in automated decision-making systems.
Understand the Science
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. Built on a large language model trained via reinforcement learning, InvEvolve can process demand data together with additional numerical and textual features, and generates white-box inventory policies with statistical safety guarantees for future deployment. We further introduce a unified framework with theoretical guarantees that connects training, inference, and deployment. This allows us to derive a lower bound on the probability that InvEvolve evolves a statistically safe and improved policy, and to characterize the multi-period performance gap relative to the oracle-safe benchmark. Tested on both synthetic data and real-world retail data, InvEvolve outperforms classical inventory policies and deep-learning-based methods. In canonical inventory settings, it generates new policies that outperform existing benchmarks.