Airline Crew Scheduling Using Potts Mean Field Techniques
摘要
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 [LU TP 97-11]. 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 techniques, a brief review is given of recent advances in their application to resource allocation problems.
引用
@article{arxiv.cond-mat/9706141,
title = {Airline Crew Scheduling Using Potts Mean Field Techniques},
author = {M. Lagerholm and C. Peterson and B. Söderberg},
journal= {arXiv preprint arXiv:cond-mat/9706141},
year = {2016}
}
备注
24 pages LaTeX, 8 ps figures