OptiGuide provides a framework for integrating Generative AI (GenAI) into optimization and decision intelligence tasks, specifically targeting supply chain optimization and mixed-integer linear programming (MILP). It offers researchers and practitioners tools to leverage large language models (LLMs) for complex problem-solving, enabling what-if analysis and exploring foundation models for MILP.

How It Works

The project leverages large language models (LLMs) to enhance decision-making processes. For supply chain optimization, it facilitates "what-if" analysis by integrating LLM capabilities. In the MILP domain, it explores the development of foundation models, aiming to improve the efficiency and effectiveness of solving complex linear programming problems through advanced AI techniques.

Highlighted Details

• Open-sourced code and data for the "OptiGuide: Large Language Models for Supply Chain Optimization" paper.
• Open-sourced code and data for the "MILP-Evolve: Towards Foundation Models for Mixed Integer Linear Programming" paper.
• Includes safeguard mechanisms for fairness, robustness, and safety in AI-driven decision-making.
• Prohibits scraping repository content for training ML models to maintain evaluation integrity.

Maintenance & Community

This project is from Microsoft and welcomes contributions, requiring agreement to a Contributor License Agreement (CLA). It adheres to the Microsoft Open Source Code of Conduct.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README, but it is a Microsoft project. The prohibition on scraping for model training suggests a focus on evaluation rather than data extraction for external model development.

Limitations & Caveats

The framework inherits the strengths and limitations of the underlying publicly available language models, including potential biases and adversarial vulnerabilities. Users must critically assess and interpret AI-driven solutions, understanding that unexpected behaviors can still arise.