Experimental study of a neural model for scheduling job shops

Most neural network models for solving the job-shop scheduling problem (JSP) are energy based and the networks usually take a long time to converge to solutions. We previously proposed (1994) a new neural model called the job-shop scheduling neural networks (JSSNNs), which need no special convergence procedure to be performed 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 batch sizes of the jobs. This may complicate the implementation. In this paper the model is extended to solve this problem. In this extended model, the number of neurons are fixed for different batch sizes. This approach can obtain solutions that are better than, or as good as those in prior work. Furthermore, in this new model, mn(n+7) number of neurons are needed to solve an n-job m-machine problem with an arbitrary batch size for each job. We presents the simulation results of this new type of neural network and compare it with some heuristic dispatching rules for appraising its quality