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dc.contributor.authorBeskow, Gregory Johnen_US
dc.description.abstractPublic dependence on air transportation has grown to its largest point in history. Along with this increased dependence is a heightened awareness of safety concerns and the need for pilots to cover all scheduled flights. All commercial pilots are certified for the particular aircraft they are flying by satisfactorily completing a Federal Aviation Administration (FAA) approved course. During the training course, a number of training devices are used including a Full Flight Simulator (FFS) and a Flight Training Device (FTD). The procurement, installation, operation, and maintenance costs of these devices are expensive. In addition, the amount of time pilots spend in training is costly because they continue to be paid their salary rate although they are unable to fly revenue-generating flights for the airline. Due to these high costs, training is scheduled very tightly, with the goals of maximizing training device utilization and minimizing the pilot's training footprint (time spent in training). Any disruption to the tight schedule, such as a simulator breakdown, or pilot illness, renders the original schedule obsolete and demands rescheduling of activities. In this research, the rescheduling problem is investigated through the development and application of several different rescheduling approaches. The problem is decomposed by first investigating a single resource model and insights gained from this experimentation are transferred to the multiple resource model. The solution approaches developed for experimentation include: right-shift rescheduling (RSR), rescheduling of affected activities (RAFF), and rescheduling of all activities (RALL). Performance measures used to compare the various approaches include the minimization of the pilot footprint, the minimization of pilot tardiness, and the minimization of the deviation that a revised schedule has from the original schedule. A case study and a series of experiments involving random disruptions to original schedules were used to analyze the solution approaches. For the data sets analyzed, the RAFF algorithm outperformed other methods with respect to the majority of the measure collected. Analysis performed on the amount of slack in the original schedule revealed that diminishing returns were observed beyond a certain level of slack. Further analysis on the impact of the location of this slack showed that the majority of the slack should be placed at the end of the schedule, or the slack should be dispersed almost evenly over the entire schedule.en_US
dc.publisherVirginia Techen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectpilot trainingen_US
dc.titleRescheduling of Airline Pilot Training Activities Following Disruptionsen_US
dc.contributor.departmentIndustrial and Systems Engineeringen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeememberKoelling, Charles Patricken_US
dc.contributor.committeecochairEllis, Kimberly P.en_US
dc.contributor.committeecochairBish, Ebru K.en_US

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