Analysis of Airline Operations using Linear Programming
This project delves into the intricacies of aircraft selection and flight frequency distribution to minimize daily operational costs for a budget airline. Leveraging the Analytic Hierarchy Process (AHP) for aircraft selection and Linear Programming (LP) for route optimization, we achieved significant cost reductions across 19 domestic destinations.
Introduction:
Welcome to our latest project where we delve into the world of aviation and operations optimization. In a fiercely competitive airline industry, the key to success lies in providing affordable fares to passengers while keeping operational costs at a minimum. Our project explores this challenge by combining analytical methods and real-world insights to streamline aircraft selection and route planning.
Aircraft Selection with Analytic Hierarchy Process (AHP):
The first crucial step in our project was to identify the most suitable aircraft for daily operations. We achieved this using the Analytic Hierarchy Process (AHP), a powerful decision-making tool that considers essential factors such as passenger capacity, maintenance cost, and aircraft pricing. Our analysis revealed that the A330-200 emerged as the top choice due to its exceptional passenger capacity, and cost-efficiency.
However, we recognized that not all airports in the Philippines could accommodate the A330-200, emphasizing the importance of aircraft operational compatibility with destination airports.
Route Optimization with Linear Programming (LP):
With the aircraft type determined, the next challenge was to optimize our daily operations. We employed Linear Programming (LP), a mathematical optimization technique, to create an efficient flight frequency distribution per aircraft type per route across nineteen domestic destinations.
Our LP model aimed to meet passenger demand at the lowest operational cost. It led us to some fascinating discoveries – the A330-200, despite having higher operational costs, proved highly cost-effective due to its substantial seating capacity, efficiently meeting high demand. Meanwhile, the A321 NEO's operational capacity was underutilized, allowing us to reduce its fleet size while still serving its purpose.
The LP model not only helped allocate the right aircraft to the right destinations but also determined the appropriate fleet size for our operational setting.
Conclusion:
In summary, our project showcases how a strategic blend of the Analytic Hierarchy Process (AHP) and Linear Programming (LP) can empower airlines to make data-driven decisions, leading to more efficient operations and substantial cost savings. The journey doesn't stop here; we're committed to exploring more ways to refine and enhance the airline industry's decision-making processes.