This research project extends an existing system called OptiChat, developed at Purdue University, to help users understand complex optimization problems through natural language explanations. At its core, OptiChat leverages large language models (LLMs) to translate mathematical solutions and constraints into accessible insights for non-experts, thereby reducing the “black box” perception of advanced optimization techniques. Previously, OptiChat focused on deterministic models with static parameters. This project seeks to broaden its capability to address online optimization scenarios that involve uncertain or evolving data, such as emergency response, dynamic job scheduling in cloud computing, and real-time supply chain disruptions.

By integrating stochastic programming approaches, the project will enable OptiChat to provide transparent and understandable decision rules for various business-critical processes. Through the development of a simulation environment reflecting Oracle’s real-world use cases, we aim to demonstrate how changing conditions or unexpected events can influence the best course of action. This collaboration between Oracle Lab and Purdue will not only enhance the tool’s technical sophistication but also elevate Oracle’s offerings in interpretable and trustworthy AI-driven optimization.