LLM framework optimizes inventory allocation by selecting best OR formulation

Researchers have developed a novel framework utilizing a large language model (LLM) to select the most effective operations research (OR) formulation for multi-warehouse inventory allocation problems. This approach addresses the challenge that no single formulation consistently performs best across diverse operational scenarios. The framework employs supervised fine-tuning and group relative policy optimization, guided by mixed-integer programming solver evaluations, to train the LLM selector. Experiments conducted with data from JD.com demonstrated significant improvements in selection accuracy and realized allocation quality compared to existing methods. AI

IMPACT This research could lead to more efficient inventory management systems by enabling AI to dynamically select the optimal operational strategy for complex allocation problems.

RANK_REASON Academic paper detailing a novel LLM-based framework for operations research formulation selection. [lever_c_demoted from research: ic=1 ai=1.0]

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• China
• Group Relative Policy Optimization
• Identity preference optimization
• JD.com
• large language model
• mixed-integer optimization
• operations research
• supervised fine-tuning

• paper
• product

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