Airline Crew Scheduling Using Potts Mean Field Techniques

TL;DR

This paper introduces a novel Potts neural network-based method for airline crew scheduling, effectively handling complex topologies and demonstrating good results on artificial and real-world problems with manageable computational effort.

Contribution

It presents a new approach using Potts mean field techniques to solve complex airline crew scheduling problems with improved efficiency and effectiveness.

Findings

Achieves very good results across various problem sizes.

Computational time scales cubically with number of flights.

Problem solutions are obtained within minutes for realistic scenarios.

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 [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.