ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research
Journal:
arXiv
Published Date:
Jun 2, 2025
Abstract
Operations research (OR) is widely deployed to solve critical decision-making
problems with complex objectives and constraints, impacting manufacturing,
logistics, finance, and healthcare outcomes. While Large Language Models (LLMs)
have shown promising results in various domains, their practical application in
industry-relevant operations research (OR) problems presents significant
challenges and opportunities. Preliminary industrial applications of LLMs for
operations research face two critical deployment challenges: 1) Self-correction
focuses on code syntax rather than mathematical accuracy, causing costly
errors; 2) Complex expert selection creates unpredictable workflows that reduce
transparency and increase maintenance costs, making them impractical for
time-sensitive business applications. To address these business limitations, we
introduce ORMind, a cognitive-inspired framework that enhances optimization
through counterfactual reasoning. Our approach emulates human cognition,
implementing an end-to-end workflow that systematically transforms requirements
into mathematical models and executable solver code. It is currently being
tested internally in Lenovo's AI Assistant, with plans to enhance optimization
capabilities for both business and consumer customers. Experiments demonstrate
that ORMind outperforms existing methods, achieving a 9.5\% improvement on the
NL4Opt dataset and a 14.6\% improvement on the ComplexOR dataset.
