Near optimal jobshop scheduling using neural network parallel computing

A parallel algorithm based on the neural network model for a jobshop scheduling problem is presented in this paper. In manufacturing systems, it is becoming more complex to manage operations of facilities, because of many requirements and constraints such as increasing product throughput, reducing work-in-process and keeping due dates. The goal of the proposed parallel algorithm is to find a near-optimum scheduling solution for the given schedule. The proposed parallel algorithm requires N/spl times/N processing elements (neurons) where N is the number of operations. The authors' empirical study on sequential processing shows the behavior of the system.<
>