Recurrent neural network approach for cyclic job shop scheduling problem
  While cyclic scheduling is involved in numerous real-world applications,
solving the derived problem is still of exponential complexity. This paper
focuses specifically on modelling the manufacturing application as a cyclic job
shop problem and we have developed an efficient neural network approach to
minimise the cycle time of a schedule. Our approach introduces an interesting
model for a manufacturing production, and it is also very efficient, adaptive
and flexible enough to work with other techniques. Experimental results
validated the approach and confirmed our hypotheses about the system model and
the efficiency of neural networks for such a class of problems.
