Abstract
The optimization of scheduling problems is considered as a combinatorial optimization problem. Hopfield and Tank showed that some combinatorial optimization problems can be solved based on an artificial neural network system. However, their network model for solving combinatorial optimization problems often attains the local optimum solution depending on the initial state of the network. Recently, some stochastic neural network models have been proposed for the purpose of avoiding the convergence to the local optimum solution. In this paper we attempt to solve the scheduling problem for minimizing the total actual flow time by using the Gaussian machine model, which is one of the stochastic neural network models.
