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100301s2009 gw | s |||| 0|eng d |
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|a 9783540898870
|9 978-3-540-89887-0
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7 |
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|a 10.1007/978-3-540-89887-0
|2 doi
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|a TA329-348
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|a TA640-643
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|a TBJ
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|a MAT003000
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|a 519
|2 23
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|a Grosche, Tobias.
|e author.
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|a Computational Intelligence in Integrated Airline Scheduling
|c by Tobias Grosche.
|h [electronic resource] /
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| 264 |
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1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2009.
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| 300 |
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|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Studies in Computational Intelligence,
|v 173
|x 1860-949X ;
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| 505 |
0 |
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|a Introduction -- Airline Scheduling Process -- Foundations of Metaheuristics -- Integrated Airline Scheduling -- Summary, Conclusions, and FutureWork.
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| 520 |
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|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.
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| 650 |
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0 |
|a Engineering.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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0 |
|a Engineering mathematics.
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| 650 |
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0 |
|a Business planning.
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| 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/Planning.
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| 650 |
2 |
4 |
|a Automotive Engineering.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783540898863
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| 830 |
# |
0 |
|a Studies in Computational Intelligence,
|v 173
|x 1860-949X ;
|
| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-540-89887-0
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| 912 |
# |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
|