Abstract Title

Flight Optimization

Is this project an undergraduate, graduate, or faculty project?

Undergraduate

group

Poster Session; 10-minute Oral Presentation; 30 minute Workshop

Authors' Class Standing

Ada Chika - Senior Ian Mungovan - Senior Timothy Mitchell - Senior Ian Young- Senior Nicholas Gachancipa - Senior

Lead Presenter's Name

Ada Chika

Faculty Mentor Name

Mihhail Berezovski

Abstract

The use of optimization tools in business is a key ingredient in streamlining operations. In the airline industry, optimization is a very important concept as it saves airlines time and money. The purpose of this research is to develop an algorithm for flight optimization.

The optimization framework will consist of three sections which include valid paths selection, optimizer development, and machine learning implementation. The first segment identifies all possible alternatives for a given flight leg. Once a leg has been identified, the script filters down all viable alternatives using available constraints which are stored in a Structural Query Language ( SQL) relational database. The optimization portion will utilize the Dijkstra’s algorithm to identify the most profitable routes. Customer satisfaction is at the core of every business model. Keeping this in mind, the optimizer will implement machine learning to observe current trends and predict future behavior, thus, improving overall customer experience. This research will serve as a preliminary model for the development of an optimizer for OneSky (a Private Charter conglomerate).

Did this research project receive funding support (Spark or Ignite Grants) from the Office of Undergraduate Research?

No

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Flight Optimization

The use of optimization tools in business is a key ingredient in streamlining operations. In the airline industry, optimization is a very important concept as it saves airlines time and money. The purpose of this research is to develop an algorithm for flight optimization.

The optimization framework will consist of three sections which include valid paths selection, optimizer development, and machine learning implementation. The first segment identifies all possible alternatives for a given flight leg. Once a leg has been identified, the script filters down all viable alternatives using available constraints which are stored in a Structural Query Language ( SQL) relational database. The optimization portion will utilize the Dijkstra’s algorithm to identify the most profitable routes. Customer satisfaction is at the core of every business model. Keeping this in mind, the optimizer will implement machine learning to observe current trends and predict future behavior, thus, improving overall customer experience. This research will serve as a preliminary model for the development of an optimizer for OneSky (a Private Charter conglomerate).