Dynamic passenger recovery model for airline disruption management

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dc.contributor.authorMutinda, Almodad Muendo
dc.date.accessioned2017-11-30T07:03:13Z
dc.date.available2017-11-30T07:03:13Z
dc.date.issued2017
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunication and Innovation at (MSc.MTI) at Strathmore Universityen_US
dc.description.abstractAirline operations experience schedule disruptions every day. These schedule disruptions require intervention from the airline operations controllers through schedule recovery. In a hub and spoke airline network model, a disruption such as a flight cancellation can affect passenger itineraries in multiple fight legs, making it hard for airlines to re-accommodate disrupted passengers within a short time period. The current airline recovery solutions do not explicitly consider passenger recovery. This dissertation investigates the passenger recovery process by considering the challenges faced by passengers during a schedule disruption, the current solutions used to recover disrupted passengers and how a suitable solution can be designed, developed, tested and validated to ensure that it solves these challenges. Data was collected from existing records of flight schedules and passenger bookings. The data collected was used as input to an optimisation model for passenger recovery. Scrum Agile Development methodology was adopted as the software methodology for developing the solution. A proof of concept web application was developed to make passenger recovery easier and reduce operational cost and passenger delay time. An optimization model was developed based on IBM ILOG CPLEX optimiser to help solve disruptions faster. Testing was conducted by both the developer and a selected sample of airline industry users.en_US
dc.identifier.urihttp://hdl.handle.net/11071/5711
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.titleDynamic passenger recovery model for airline disruption managementen_US
dc.typeThesisen_US
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MMTI Theses and Dissertations (2017)