A review on evolution of production scheduling with neural networks

The production scheduling problem allocates limited resources to tasks over time and determines the sequence of operations so that the constraints of the system are met and the performance criteria are optimized. One approach to this problem is the use of artificial neural networks (ANNs) stand alone or in conjunction with other methods. Artificial neural networks are computational structures that implement simplified models of biological processes, and are preferred for their robustness, massive parallelism, and learning ability. In this paper, we give a comprehensive overview on ANN approaches for solution of production scheduling problems, discuss both theoretical developments and practical experiences, and identify research trends. More than 50 major production and operations management journals published in years 1988–2005 have been reviewed. Existing approaches are classified into four groups, and additionally a historical progression in this field was emphasized. Finally, recommendations for future research are suggested in this paper.