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. 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.
Original language | English |
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Pages (from-to) | 81-96 |
Number of pages | 16 |
Journal | European Journal of Operational Research |
Volume | 120 |
Issue number | 1 |
Publication status | Published - 2000 Jan 1 |
Free keywords
- Neural networks
- Optimization
- Transportation