Abstract
Job-shop scheduling is usually a strongly NP-hard problem of combinatorial optimization problems and is one of the most typical production scheduling problem. It is usually very hard to find its optimal solution. In this paper, a new hybrid approach in dealing with this job-shop scheduling problem based on artificial neural network and genetic algorithm (GA) is presented. The GA is used for optimization of sequence and neural network (NN) is used for optimization of operation start times with a fixed sequence. New type of neurons which can represent processing restrictions and resolve constraint conflict are defined to construct a constraint neural network (CNN). CNN with a gradient search algorithm is applied to the optimization of operation start times with a fixed processing sequence. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhao, F., Hong, Y., Yu, D., Chen, X., Yang, Y. (2005). Integration of Artificial Neural Networks and Genetic Algorithm for Job-Shop Scheduling Problem. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_123
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DOI: https://doi.org/10.1007/11427391_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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