Neural Network Model Based Job Scheduling and Its Implementation in Networked Manufacturing

Abstract

On analysis of the workshop management characteristics of discrete enterprises in networked manufacturing environment, an instruction scheduling management system was designed and developed based on the discrete Hopfield network model. While changing weight factors or thresholds of neural network under associative memory mode, a suitable model for job scheduling was designed, which will bring advantages of neural network into play. The scheduling results were analyzed with multi-objective optimization computing method. Then this paper presented the comparison of simulation results and actual scheduling data,which shown that neural network scheduling model tends to consider evaluation indicators comprehensively, and all indicators of the corresponding scheduling solution keep balance.