A neural network model for the job-shop scheduling problem with the consideration of lot sizes

This paper presents an application of neural networks for solving the job-shop scheduling problem with the consideration of lot sizes (i.e. job batch sizes), which are important since jobs are often processed in batches. The energy-based neural network that have been proposed to solve this problem usually take a long time to converge to solutions. The authors previously (1994) proposed a new neural model which needs no special convergence procedure and can find optimal or near-optimal solutions of the problem at a much faster speed. However, in this model as well as other energy-based models, the number of neurons are proportional to the lot sizes of the jobs. This may complicate the implementation. In this paper, we extend our model to solve this problem. In this extended model, the number of neurons are fixed for different lot sizes. These results are quite good in terms of quality and speed. Furthermore, in this new model, mn(n+7) number of neurons are needed to solve an n-job m-machine problem with an arbitrary lot size for each job.