COMPLEX SCHEDULING WITH POTTS NEURAL NETWORKS
L. Gislen, C. Peterson and B. S\"oderberg
Abstract: In a recent paper (Gisl\'{e}n, Peterson and S\"oderberg 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems
using a Potts neural network was developed and numerically explored on simplified
and synthetic problems.
In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the
interaction of Potts neurons with different number of components. We analyze
the corresponding linearized mean field equations with respect to estimating the
phase transition temperature. Also a brief comparison with the linear
programming approach is given.
Testbeds consisting of generated problems within the Swedish high school system
are solved efficiently with high quality solutions as results.
LU TP 91-5