Airline crew scheduling using Potts mean field techniques

Martin Lagerholm, Carsten Peterson, Bo Söderberg

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)81-96
Number of pages16
JournalEuropean Journal of Operational Research
Issue number1
Publication statusPublished - 2000 Jan 1

Free keywords

  • Neural networks
  • Optimization
  • Transportation


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