Airline crew scheduling using Potts mean field techniques
(2000) In European Journal of Operational Research 120(1). p.81-96- Abstract
A novel method is presented and explored within the framework of Potts neural networks for solving optimization problems with a non-trivial topology, with the airline crew scheduling problem as a target application. The key ingredient to handle the topological complications is a propagator defined in terms of Potts neurons. The approach is tested on artificial problems generated with two real-world problems as templates. The results are compared against the properties of the corresponding unrestricted problems. The latter are subject to a detailed analysis in a companion paper (M. Lagerholm, C. Peterson, B. Söderberg, submitted to European Journal of Operational Research). Very good results are obtained for a variety of problem sizes.... (More)
A novel method is presented and explored within the framework of Potts neural networks for solving optimization problems with a non-trivial topology, with the airline crew scheduling problem as a target application. The key ingredient to handle the topological complications is a propagator defined in terms of Potts neurons. The approach is tested on artificial problems generated with two real-world problems as templates. The results are compared against the properties of the corresponding unrestricted problems. The latter are subject to a detailed analysis in a companion paper (M. Lagerholm, C. Peterson, B. Söderberg, submitted to European Journal of Operational Research). Very good results are obtained for a variety of problem sizes. The computer time demand for the approach only grows like (number of flights)3. A realistic problem typically is solved within minutes, partly due to a prior reduction of the problem size, based on an analysis of the local arrival/departure structure at the single airports. To facilitate the reading for audiences not familiar with Potts neurons and mean field (MF) techniques, a brief review is given of recent advances in their application to resource allocation problems.
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- author
- Lagerholm, Martin LU ; Peterson, Carsten LU and Söderberg, Bo LU
- organization
- publishing date
- 2000-01-01
- type
- Contribution to journal
- publication status
- published
- keywords
- Neural networks, Optimization, Transportation
- in
- European Journal of Operational Research
- volume
- 120
- issue
- 1
- pages
- 16 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:0004654743
- ISSN
- 0377-2217
- language
- English
- LU publication?
- yes
- id
- f62157f1-7ef3-4dbc-89cc-4aaae22669c6
- date added to LUP
- 2016-10-03 19:09:44
- date last changed
- 2024-01-04 13:37:32
@article{f62157f1-7ef3-4dbc-89cc-4aaae22669c6, abstract = {{<p>A novel method is presented and explored within the framework of Potts neural networks for solving optimization problems with a non-trivial topology, with the airline crew scheduling problem as a target application. The key ingredient to handle the topological complications is a propagator defined in terms of Potts neurons. The approach is tested on artificial problems generated with two real-world problems as templates. The results are compared against the properties of the corresponding unrestricted problems. The latter are subject to a detailed analysis in a companion paper (M. Lagerholm, C. Peterson, B. Söderberg, submitted to European Journal of Operational Research). Very good results are obtained for a variety of problem sizes. The computer time demand for the approach only grows like (number of flights)<sup>3</sup>. A realistic problem typically is solved within minutes, partly due to a prior reduction of the problem size, based on an analysis of the local arrival/departure structure at the single airports. To facilitate the reading for audiences not familiar with Potts neurons and mean field (MF) techniques, a brief review is given of recent advances in their application to resource allocation problems.</p>}}, author = {{Lagerholm, Martin and Peterson, Carsten and Söderberg, Bo}}, issn = {{0377-2217}}, keywords = {{Neural networks; Optimization; Transportation}}, language = {{eng}}, month = {{01}}, number = {{1}}, pages = {{81--96}}, publisher = {{Elsevier}}, series = {{European Journal of Operational Research}}, title = {{Airline crew scheduling using Potts mean field techniques}}, volume = {{120}}, year = {{2000}}, }