Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76092
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dc.contributor.authorChung, SHen_US
dc.contributor.authorMa, HLen_US
dc.contributor.authorChan, HKen_US
dc.date.accessioned2018-05-10T02:55:20Z-
dc.date.available2018-05-10T02:55:20Z-
dc.date.issued2017-
dc.identifier.citationRisk analysis, 2017, v. 37, no. 8, special issue SI, p. 1443-1458en_US
dc.identifier.issn0272-4332-
dc.identifier.urihttp://hdl.handle.net/10397/76092-
dc.description.abstractThis article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era.en_US
dc.description.sponsorshipDepartment of Industrial and Systems Engineeringen_US
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.relation.ispartofRisk analysisen_US
dc.subjectBig dataen_US
dc.subjectFlight reliabilityen_US
dc.subjectRobust crew pairingen_US
dc.titleCascading delay risk of airline workforce deployments with crew pairing and schedule optimizationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1443-
dc.identifier.epage1458-
dc.identifier.volume37-
dc.identifier.issue8-
dc.identifier.doi10.1111/risa.12746-
dc.identifier.isiWOS:000407612600002-
dc.identifier.pmid27935094-
dc.identifier.eissn1539-6924-
dc.identifier.rosgroupid2017004222-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201805 bcrc-
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