A neural network architecture for faster dynamic scheduling in manufacturing systems

Computation of optimum schedules for dynamic scheduling of tasks introduces an overhead delay in the processing time of the programs executed in an automated system. This is particularly so if serial scheduling algorithms are used. It is proposed that tasks be dynamically scheduled with the Lawler scheduling algorithm and that, to minimise the additional delay due to the computation of optimum schedules, a neural network be used for retrieving optimal solutions. A neural network architecture that can recognise binary vector representations of scheduling problems and retrieve optimal schedules in negligible time is described