Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2019 Airline fleet planning and utilization hours comparison studies Daniel Zhou Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Aerospace Engineering Commons Recommended Citation Zhou, Daniel, "Airline fleet planning and utilization hours comparison studies" (2019). Graduate Theses and Dissertations. 17131. https://lib.dr.iastate.edu/etd/17131 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Airline fleet planning and utilization hours comparison studies by Daniel Xiaoyang Zhou A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Aerospace Engineering Program of Study Committee: Peng Wei, Major Professor Leifur T. Leifsson Lizhi Wang The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred. Iowa State University Ames, Iowa 2019 Copyright c Daniel Xiaoyang Zhou, 2019. All rights reserved. ii DEDICATION I would like to dedicate this thesis to my Mum and Dad without whose support I would not have been able to complete this work. I would also like to thank my friends and family for their loving guidance and financial assistance during the writing of this work. iii TABLE OF CONTENTS Page LIST OF TABLES . .v LIST OF FIGURES . vi ACKNOWLEDGMENTS . ix NOMENCLATURE . .x ABSTRACT . xii CHAPTER 1. OVERVIEW . .1 1.1 Introduction . .1 CHAPTER 2. PROBLEM STATEMENT . .5 CHAPTER 3. AIRPLANE UTILIZATION AND TREND STUDY . .6 3.1 Method . .6 3.2 Results . 11 3.2.1 Utilization Hour Comparison of Tail Numbers . 11 3.2.2 Average Utilization Hour Comparison Between Legacy Carriers and Low- Cost Carriers . 18 CHAPTER 4. AIRPLANE UTILIZATION PREDICTION VIA MACHINE LEARNING MODELS . 22 4.1 Method . 22 4.1.1 K-Fold Cross-Validation . 24 4.1.2 Preliminary Results . 25 4.1.3 Random Forest Model . 27 4.1.4 Support Vector Regression Model . 28 iv 4.1.5 Neural Network Model . 30 4.1.6 Boosting Models . 32 4.2 Results . 34 4.2.1 Random Forest Algorithm Prediction Results . 35 4.2.2 Support Vector Regression Algorithm Prediction Results . 38 4.2.3 Neural Network Prediction Results . 45 4.2.4 Boosting Algorithms Prediction Results . 51 4.2.5 Summary of Machine Learning Model Predictions . 58 CHAPTER 5. CONCLUSION AND FUTURE WORK . 60 BIBLIOGRAPHY . 62 v LIST OF TABLES Page Table 3.1 A Summary of AOTP Database Fields . .6 Table 4.1 Machine Learning Model Predictions . 58 vi LIST OF FIGURES Page Figure 3.1 Erroneous Data in Raw Data Example I . .8 Figure 3.2 Erroneous Data in Raw Data Example II . .8 Figure 3.3 Time Zone Problem in Raw Data Example . .9 Figure 3.4 American Airlines A319 Single Tail Number Comparison 2014 . 11 Figure 3.5 American Airlines A319 Single Tail Number Comparison 2017 . 12 Figure 3.6 Frontier Airlines A319 Single Tail Number Comparison 2014 . 12 Figure 3.7 Frontier Airlines A319 Single Tail Number Comparison 2017 . 13 Figure 3.8 American Airlines B737 Single Tail Number Comparison 2014 . 14 Figure 3.9 American Airlines B737 Single Tail Number Comparison 2017 . 15 Figure 3.10 Southwest Airlines B737 Single Tail Number Comparison 2014 . 15 Figure 3.11 Southwest Airlines B737 Single Tail Number Comparison 2017 . 16 Figure 3.12 A319 American Airlines vs Frontier Airlines 2014 . 18 Figure 3.13 A319 American Airlines vs Frontier Airlines 2017 . 19 Figure 3.14 B737 American Airlines vs Southwest Airlines 2014 . 19 Figure 3.15 B737 American Airlines vs Southwest Airlines 2017 . 20 Figure 4.1 Pycharm Library Menu . 23 Figure 4.2 Pycharm Console . 23 Figure 4.3 Hyperplane On Linearly Separable Data . 29 Figure 4.4 A Neural Network . 30 Figure 4.5 Gradient Boosting Error Fitting . 33 Figure 4.6 Number of Estimators Vs. Max Features . 35 Figure 4.7 Number of Estimators Vs. Minimum Samples at Terminal Node . 35 vii Figure 4.8 Max Features Vs. Minimum Samples at Terminal Node . 36 Figure 4.9 Preliminary Results Vs. Final Results . 36 Figure 4.10 Prediction Results Vs. Baseline Results . 37 Figure 4.11 Cost Vs. Epsilon Linear Kernel . 38 Figure 4.12 Cost Vs. Epsilon Linear Kernel Normalized . 38 Figure 4.13 Cost Vs. Epsilon RBF Kernel . 39 Figure 4.14 Cost Vs. Epsilon RBF Kernel Normalized . 39 Figure 4.15 Cost Vs. Gamma RBF Kernel . 40 Figure 4.16 Cost Vs. Gamma RBF Kernel Normalized . 40 Figure 4.17 Gamma Vs. Epsilon RBF Kernel . 41 Figure 4.18 Gamma Vs. Epsilon RBF Kernel Normalized . 41 Figure 4.19 Preliminary Results Vs. Final Results Linear Kernal . 42 Figure 4.20 Preliminary Results Vs. Final Results RBF Kernal . 42 Figure 4.21 Prediction Results Vs. Baseline Results Linear Kernel . 43 Figure 4.22 Prediction Results Vs. Baseline Results RBF Kernel . 43 Figure 4.23 SGD Optimizer . 45 Figure 4.24 RMSProp Optimizer . 46 Figure 4.25 Adagrad Optimizer . 46 Figure 4.26 Adadelta Optimizer . 47 Figure 4.27 Adam Optimizer . 47 Figure 4.28 Adamax Optimizer . 48 Figure 4.29 Nadam Optimizer . 48 Figure 4.30 Preliminary Results Vs. Final Results Adam Optimizer . 49 Figure 4.31 Prediction Results Vs. Baseline Results Adam Optimizer . 49 Figure 4.32 Number of Estimators Vs. Learning Rate Adaptive Boosting . 51 Figure 4.33 Number of Estimators Vs. Max Depth Gradient Boosting . 51 Figure 4.34 Number of Estimators Vs. Learning Rate Gradient Boosting . 52 viii Figure 4.35 Max Depth Vs. Learning Rate Gradient Boosting . 52 Figure 4.36 Number of Estimators Vs. Learning Rate XGBoosting . 53 Figure 4.37 Max Depth Vs. Learning Rate XGBoosting . 53 Figure 4.38 Number of Estimators Vs. Max Depth XGBoosting . 54 Figure 4.39 Preliminary Results Vs. Final Results Adaptive Boosting . 54 Figure 4.40 Preliminary Results Vs. Final Results Gradient Boosting . 55 Figure 4.41 Preliminary Results Vs. Final Results XGBoost . 55 Figure 4.42 Prediction Results Vs. Baseline Results Adaptive Boosting . 56 Figure 4.43 Prediction Results Vs. Baseline Results Gradient Boosting . 56 Figure 4.44 Prediction Results Vs. Baseline Results XGBoost . 57 ix ACKNOWLEDGMENTS I would like to take this opportunity to express my thanks to those who helped me with various aspects of conducting research and the writing of this thesis. First and foremost, Dr. Peng Wei for his guidance, patience and support throughout this research and the writing of this thesis. His insights and words of encouragement have often inspired me and renewed my hopes for completing my graduate education. I would also like to thank my committee members for their efforts and contributions to this work: Dr. Leifur Leiffson and Dr. Lizhi Wang. I would additionally like to thank everyone in the Intelligent Air Systems Lab for their support, for which has guided me through research. Last, but certainly not least, I would like to thank those nearest and dearest to me, my friends and family, for their endless support during a strenuous time in my life. These people include my parents, Joe and Sharon, my sister, Ruby, and my friends Anshul, Xuxi, Xiaosong, George and Nicole, in no particular order whatsoever. x NOMENCLATURE Symbols EΘ = Expectation w.r.t Θn Θn = Random Parameter Xn = Dataset Features Dn = Dataset w = Gradient of Separating Hyperplane " = Margin of Tolerance W2 = Cumulative Weights of Upstream Nodes b2 = Bias of Downstream Node xi Abbreviations LCC = Low-Cost Carrier FAA = Federal Aviation Administration NTSB = National Transportation Safety Board RUL = Remaining Useful Life MSA = Metropolitan Statistical Area RMSE = Root Mean Squared Error BTS = Bureau of Transportation Statistics AOTP = Airline On-Time Performance PDT = Pacific Daylight Time MST = Mountain Standard Time PT = Pacific Time GBA = Gradient Boosting Algorithm IDE = Independent Development Environment XGBoost = Extreme Gradient Boost RBF = Radial Basis Function CPU = Central Processing Unit NAG = Nesterov accelerated gradient xii ABSTRACT After the latest mechanical malfunction accidents involving Allegiant [1] and Southwest Air- lines [2], 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 [3]. 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 imple- mentation 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.