Adaptive scheduling utilizing a neural network structure

Adaptive scheduling for flexible manufacturing systems using a neural network structure is described. Adaptive scheduling consists of three parts: features representation of the job orders and the factory environment, a set of fixed scheduling algorithms, and an algorithm selector. The statistics on orders is represented by a small number of feature parameters. Each of the algorithms included in the algorithm set is most suitable for a specific set of orders and factory status, but no single algorithm is best under all conditions. The algorithm selector chooses one of the algorithms to optimize some scheduling performance measure, for given values of the feature parameters. To illustrate the performance of the adaptive scheduling process, 10,000 different order cases were taken from uniformly distributed data. For the simulation, the algorithm selector was implemented using a backpropagation neural network structure. The results of the computer experiments demonstrated the superiority of adaptive scheduling over all of the seven fixed scheduling strategies