Heuristic combined artificial neural networks to schedule hybrid flow shop with sequence dependent setup times

Authors:
Lixin Tang;Yanyan Zhang
Affiliations:
Department of Systems Engineering, Northeastern University, Shenyang, Liaoning, China;Department of Systems Engineering, Northeastern University, Shenyang, Liaoning, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

This paper addresses the problem of arranging jobs to machines in hybrid flow shop in which the setup times are dependent on job sequence. A new heuristic combined artificial neural network approach is proposed. The traditional Hopfield network formulation is modified upon theoretical analysis. Compared with the common used permutation matrix, the new construction needs fewer neurons, which makes it possible to solve large scale problems. The traditional Hopfield network running manner is also modified to make it more competitive with the proposed heuristic algorithm. The performance of the proposed algorithm is verified by randomly generated instances. Computational results of different size of data show that the proposed approach works better when compared to the individual heuristic with random initialization.