Computational Intelligence in Integrated Airline Scheduling

An airline schedule represents the central planning element of each airline. In general, the objective of airline schedule optimization is to find the airline schedule that maximizes operating profit. This planning task is not only the most important but also the most complex task an airline is conf...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Grosche, Tobias (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
Σειρά:Studies in Computational Intelligence, 173
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03228nam a22005415i 4500
001 978-3-540-89887-0
003 DE-He213
005 20151204152730.0
007 cr nn 008mamaa
008 100301s2009 gw | s |||| 0|eng d
020 |a 9783540898870  |9 978-3-540-89887-0 
024 7 |a 10.1007/978-3-540-89887-0  |2 doi 
040 |d GrThAP 
050 4 |a TA329-348 
050 4 |a TA640-643 
072 7 |a TBJ  |2 bicssc 
072 7 |a MAT003000  |2 bisacsh 
082 0 4 |a 519  |2 23 
100 1 |a Grosche, Tobias.  |e author. 
245 1 0 |a Computational Intelligence in Integrated Airline Scheduling  |h [electronic resource] /  |c by Tobias Grosche. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2009. 
300 |a XX, 250 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 173 
505 0 |a Airline Scheduling Process -- Foundations of Metaheuristics -- Integrated Airline Scheduling -- Summary, Conclusions, and Future Work. 
520 |a An airline schedule represents the central planning element of each airline. In general, the objective of airline schedule optimization is to find the airline schedule that maximizes operating profit. This planning task is not only the most important but also the most complex task an airline is confronted with. Until now, this task is performed by dividing the overall planning problem into smaller and less complex subproblems that are solved separately in a sequence. However, this procedure is only of minor capability to deal with interdependencies between the subproblems, resulting in less profitable schedules than those being possible with an approach solving the airline schedule optimization problem in one step. In this work, two planning approaches for integrated airline scheduling are presented. One approach follows the traditional sequential approach: existing models from literature for individual subproblems are implemented and enhanced in an overall iterative routine allowing to construct airline schedules from scratch. The other planning appraoch represents a truly simultaneous airline scheduling: using metaheuristics, airline schedules are processed and optimized at once without a separation into different optimization steps for its subproblems. 
650 0 |a Engineering. 
650 0 |a Organization. 
650 0 |a Planning. 
650 0 |a Artificial intelligence. 
650 0 |a Applied mathematics. 
650 0 |a Engineering mathematics. 
650 0 |a Automotive engineering. 
650 1 4 |a Engineering. 
650 2 4 |a Appl.Mathematics/Computational Methods of Engineering. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Organization. 
650 2 4 |a Automotive Engineering. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783540898863 
830 0 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 173 
856 4 0 |u http://dx.doi.org/10.1007/978-3-540-89887-0  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)