Degree Type

Thesis

Date of Award

2019

Degree Name

Master of Science

Department

Aerospace Engineering

Major

Aerospace Engineering

First Advisor

Peng Wei

Abstract

After the latest mechanical malfunction accidents involving Allegiant and Southwest Airlines, a special interest was taken to investigate whether low-cost carriers (LCC) are taking an overly aggressive stance in regards to the utilization of aircraft within their respective fleets. Based on summary reports obtained from incident logs generated by the FAA (Federal Aviation Administration) and NTSB (National Transportation Safety Board), it was observed that Allegiant Airlines was almost three and a half times as likely to encounter a mid-air breakdown as legacy carriers are. On the economic front, the fallout that Southwest Airlines has faced from the Flight 1380 incident after an engine fan blade sheared may very likely have been a potential factor that led to an immediate decline in ticket reservations.

From a cost savings perspective, figures from a forecast analysis conducted by ICF International in 2015 predict a 40 percent increase in total fleet size across all airlines combined in the world between the years 2015 and 2025. With a global fleet size approaching 40,000 aircraft by the year 2025, the use of historical utilization data could play a key factor towards profit maximization in strategic forecasting for airline maintenance and fleet planning through the study and implementation of past trends; historical data could assist airlines with making more informed decisions on fleet planning and maintenance scheduling, by taking into consideration past patterns as well as seasonality effects in the planning process.

This study will look at airplane utilization of legacy carriers and LCC’s in their short-haul and medium-haul operations, and draw comparisons between each respective airline, focusing on aircraft types which are dedicated towards flying such routes. The objective is to explore the potential connection between airlines observing higher airplane utilization and higher frequency of accidents, with airlines that observe lower airplane utilization and frequency of accidents. These observations will likely serve to suggest whether LCC’s are utilizing their fleets too aggressively, and to find common patterns and seasonality across all airlines that can be used as a general guideline towards maintenance scheduling and fleet planning; one that incorporates safe flying practices while maximizing profits simultaneously.

In addition, this study will utilize machine learning algorithms in an effort to explain the utilization patterns observed in historical data. The aim is to determine the potential of using historical utilization hours to capture the results produced by airline fleet planning models, and in addition, to test the feasibility and effectiveness of using machine learning algorithms as an accurate forecasting tool in establishing airplane utilization models. Current research in the airline industry address problems and situations ranging from forecasting future fleet demands, to forecasting passenger load factors, but with less emphasis placed on problems that could benefit from an accurate utilization model; such problems could be used in applications such as fleet planning and maintenance scheduling by the fleet planning and maintenance scheduling departments of respective airlines. This study aims to fill the void, and analyze airplane utilization via the use of historical airline data and various machine learning algorithms.

Copyright Owner

Daniel Zhou

Language

en

File Format

application/pdf

File Size

76 pages

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