Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1584
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dc.contributor.authorWeerasinghe, P.S.R.-
dc.contributor.authorRanasinghe, R.A.M.D.K.-
dc.contributor.authorMahanthe, M.M.V.R.B.-
dc.contributor.authorSamarakoon, P.G.C.B.-
dc.contributor.authorRankothge, W.H.-
dc.contributor.authorKasthurirathna, D.-
dc.date.accessioned2022-03-14T05:12:28Z-
dc.date.available2022-03-14T05:12:28Z-
dc.date.issued2020-12-10-
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1584-
dc.description.abstractWith close to 4 billion origin-destination passenger journeys worldwide, airline operations have become a crucial factor in the global economy. With the increasing number of journeys and passengers, managing the daily operations of airlines have become a complicated task. We have proposed a real-time decision optimization platform for airline operations with the following subsystems: (1) determine the optimum path for a flight, (2) optimum fleet assignment, (3) optimum gate allocation, (4) optimum crew allocation. We have used an approximation (heuristics) based optimization approach: Genetic Programming (GP) to implement the modules. The results of our proposed platform illustrate that, the decision-making process of Airline Operations Control Center (AOCC) can be optimized, and dynamic change requirements can be accommodated.en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;-
dc.subjectAirline Industryen_US
dc.subjectGenetic Algorithmen_US
dc.subjectPlanningen_US
dc.subjectSchedulingen_US
dc.subjectOptimizationen_US
dc.titleReal-Time Decision Optimization Platform for Airline Operationsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357157en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020

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