Airline Payload and Fuel Optimization using Machine Learning in MATLAB

Overview

This project, Airline Payload and Fuel Optimization, leverages the power of MATLAB's Machine Learning Toolbox to optimize airline revenue and cost efficiency. It focuses on maximizing income by strategically managing passenger numbers, cargo, flight timings, and fuel uplift for round trips. This optimization is crucial in the context of fluctuating ticket prices and fuel costs in the airline industry.

Key Features

• Data-Driven Modeling: Utilizes real-world airline data to create models that estimate fuel burn, integrating features such as passenger count, cargo weight, and flight schedules.
• Machine Learning Integration: Employs the MATLAB Regression Learner app, with Support Vector Machine (SVM) models showing excellent performance in initial tests.
• Optimization Techniques: Applies MATLAB's Optimization Toolbox to explore the global maximum income. Techniques like Genetic Algorithm, Random Search, Pattern Search, and Fmincon are demonstrated for effective optimization.

Educational Demonstration

This project will serve as an educational tool in 2021, simplifying certain aspects for demonstrating to students. It doesn't consider all real-world aviation constraints and regulations, offering a foundation to build more complex features.

YouTube Tutorial Series

Explore the project in action through our two-part YouTube series:

• Part 1: Introduction to Model Building
• Part 2: Optimization Techniques in Practice

Resources

• MathWorks File Exchange: Payload_Fuel_Optimizer
• GitHub Repository: Payload_Fuel_Optimizer GitHub

Keywords

• Airline Revenue Optimization
• Fuel and Ticket Price Optimization
• MATLAB Machine Learning Toolbox
• Regression Learner
• SVM Models
• Cost Optimization in Airlines
• Artificial Intelligence in Aviation
• MATLAB Optimization Toolbox
• Genetic Algorithm, Random Search, Pattern Search in MATLAB

Contributing

Contributions to enhance the project are welcome.