JSM 2013 Home
Online Program Home
My Program

Abstract Details

Activity Number: 91
Type: Contributed
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #310069
Title: Pilots' Absence Prediction in an Airline Company
Author(s): Amir Hosein Homaie Shandizi*+ and Bruno Agard and Michel Gamache and Vahid Partovi Nia
Companies: and Ecole Polytechnique de Montreal and Ecole Polytechnique de Montreal and École Polytechnique Montréal
Keywords: Supervised Learning ; Data Mining Algoritm ; Real Data Modeling
Abstract:

One of the most important problems in airline flight deck crew scheduling is determining the optimum number of reserves required to cover operational needs. One of the main reasons for operational changes in flight deck crew is pilot absenteeism. This depends on different factors including the type of flight, pilot?s personal characteristics and the structure of the block month. Real data from an airline company was used to create a decision analysis tool for predicting pilot absenteeism at the strategic and tactical level. The proposed decision analysis tool is a supervised learning algorithm that combines different methods of time series analysis and association rules to determine the principal components which make pilot absenteeism predictable.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2013 program




2013 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.