Recurrent neural network approach for cyclic job shop scheduling problem
This addresses scheduling challenges in manufacturing, but appears incremental as it applies neural networks to an existing problem class.
The paper tackled the cyclic job shop scheduling problem in manufacturing by developing an efficient neural network approach to minimize cycle time, with experimental results validating the model and efficiency.
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.