AI is My Co-Pilot: Did ChatGPT Just Solve the Oldest Travel Riddle?
In an age where technology seamlessly intertwines with our daily lives, the realm of airline network management is no exception. In a moment of whimsy, I enlisted ChatGPT to address a classic challenge in optimization: the Traveling Salesman Problem (TSP).
At its core, the TSP involves determining the most efficient route that visits a series of locations and returns to the starting point. It’s a problem that has perplexed mathematicians and computer scientists for decades, yet it finds practical application in modern airline route optimization, especially in point-to-point networks.
Airline Network Models: Point-to-Point vs. Hub-and-Spoke
In the airline industry, choosing a network model is a pivotal decision. While the hub-and-spoke model centralizes traffic, the point-to-point model offers a direct route option, crucial for smaller airlines seeking operational efficiency and customer satisfaction. I chose to model this when generating some mock (or faked) traffic.
The Challenge
Crafting a realistic mock airline network was my primary goal. I needed data: flight schedules, aircraft types, city pairs — the works — over a series of years.
