Complex Scheduling with Potts Neural Networks

In a recent paper (Gislén et al. 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.