Student: Staskevich Gennady
Advisor: Dr. Joseph Skufca
PhD Committee members: Dr. Christopher Lynch, Dr. Faraz Hussain, Dr. Chen Liu, Dr. Bryant Wysocki
Date: Friday May 9, 2025, 10 am
Location: Zoom, ID: 7127480834 https://clarkson.zoom.us/j/7127480834
Abstract:
This research focuses on developing an innovative, generalized process to address edge cases of Maximum On Ground (MOG) congestion at remote locations using Large Language Models (LLMs). MOG optimization refers to the critical task of managing aircraft operations on and around airfields worldwide. Effective MOG scheduling is a cornerstone of the Air Mobility Command (AMC) airlift mission, particularly in far-forward, resource-constrained environments where operational efficiency is paramount.
The primary objective of this work is to create intuitive workflows that empower Air Force operators with basic computer science skills to rapidly design and implement practical, tailored solutions for time-sensitive scheduling challenges. These solutions must account for the unique constraints of remote operations, including limited infrastructure, dynamic operational conditions, and restricted access to advanced computing resources, and advanced software development skills.
Key assumptions guiding this research include:
- The operational environment is remote, characterized by resource constraints such as limited personnel, infrastructure, and connectivity.
- The situation on the ground is highly dynamic, with significant uncertainties and evolving conditions.
- Time is a critical factor, as delays in decision-making can sharply reduce the viability of potential solutions.
- Access to cloud computing and advanced computational resources may be restricted or unavailable due to the remote nature of operations.
- Traditional methods for developing tailored scheduling solutions are often prohibitively expensive, time-intensive, and resource-demanding.
This research aims to bridge these gaps by leveraging LLMs as a tool for rapid solution generation, enabling operators to adapt quickly to changing conditions while maintaining mission effectiveness. The ultimate goal is to enhance the Air Force’s ability to respond effectively to MOG congestion challenges in austere, remote environments.