Leveraging AI for Transparent Flight Disruption Management

The key takeaway from the article is that combining large language models (LLMs) like GPT-3 with conventional optimization algorithms through function calling provides an effective way to build conversational agents that can provide optimal solutions for managing flight disruptions. This improves transparency and explainability of recommendations.


  • The article proposes using LLMs along with optimization algorithms to create a disruption management system to handle flight delays and cancellations.
  • Azure function calling is used to expose the LLM to the optimization backend and improve its capabilities.
  • Two key functions are created - a Disruption Management System (DMS) function that provides optimal recovery paths, and a Cost function to explain decisions.
  • An API call interfaces the LLM with these functions, calls them based on user queries, augments responses and provides final responses.
  • Accuracy of responses is improved through prompt engineering and optimizing context.
  • The framework provides real-time, optimal solutions for flight disruptions and brings transparency through ability to explain logic behind recommendations.
  • Challenges faced included inaccurate responses and inability to fully leverage functions. These were addressed through better function descriptions, splitting functions and enriching function outputs.
  • The solution has potential to avoid local optima and reach global optima through LLM overseeing the optimization process.


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