DocumentCode
3081371
Title
Air Cargo Scheduling Using Genetic Algorithms
Author
Fong, Simon ; Da Costa, Miguel Gomes ; Khoury, Richard
Author_Institution
Dept. of Comput. & Inf. Sci., Univ. of Macau, Macau, China
fYear
2013
fDate
24-26 Aug. 2013
Firstpage
170
Lastpage
173
Abstract
This project is to optimize the scheduling of the packages within the aircrafts´ loading capacities, which are simulated. The optimization criteria are evaluated by customer satisfaction and maximize the usage and profit of the aircrafts. Three algorithms for the batch delivery scheduling problem are developed to find the optimal air cargo shipment. These algorithms are genetic algorithm with earliest due date method, extended due date method and genetic algorithm with extended due date method. The performances of these algorithms are compared to first come first serve and earliest due date scheduling method. The performance of genetic algorithm is analyzed by its fitness function. Air cargos which are handled within Chinese cities is based on flight schedules of nine airline companies including Air Macau, EVA Airways, Cathay Pacific, China Southern Airlines, China Eastern Airlines, Air China, Dragon Air, China Airlines and Mandarin Airlines.
Keywords
aircraft; customer satisfaction; freight handling; genetic algorithms; goods distribution; scheduling; Air China; Air Macau; Cathay Pacific; China Airlines; China Eastern Airlines; China Southern Airlines; Dragon Air; EVA Airways; Mandarin Airlines; air cargo scheduling; aircraft loading capacity; batch delivery scheduling problem; customer satisfaction; earliest due date method; extended due date method; fitness function; genetic algorithm; optimal air cargo shipment; optimization criteria; Airplanes; Companies; Genetic algorithms; Job shop scheduling; Logistics; genetic algorithms; scheduling;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational and Business Intelligence (ISCBI), 2013 International Symposium on
Conference_Location
New Delhi
Print_ISBN
978-0-7695-5066-4
Type
conf
DOI
10.1109/ISCBI.2013.41
Filename
6724346
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