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.
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.
I do tend to get a little verbose when it comes to my prompts, but this is the way I can get the best output from ChatGPT. I also wasn’t sure if this would work well — it started with a chain of thoughts: “I’d really like to build a set of mock data to test a UI idea I have for a flight network”, “hmm…I wonder if ChatGPT could help get this started?”
(Also, for reference, I am not a Python developer. I had some finagling to do to get it set up on my system first.)
I would like your help in generating a python script to create a text file
with the following comma delimited information:
aircraft tail number, origin date, origin time, origin airport,
destination date, destination time, destination airport
It should generate mock data for 32 aircraft flying between 12 airports.
Each aircraft would fly to two or three airports, and then the next day
they would all start from the airport where they left off.