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An artificial neural network based heuristic for flow shop scheduling problems
Published in Springer Science and Business Media LLC
Volume: 22
Issue: 2
Pages: 279 - 288
The objective of this paper is to find a sequence of jobs in the flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the problem. The network is trained with the optimal sequences of completely enumerated five, six and seven jobs, ten machine problem and this trained network is then used to solve the problem with greater number of jobs. The sequence obtained using artificial neural network (ANN) is given as the initial sequence to a heuristic proposed by Suliman and also to genetic algorithm (GA) as one of the sequences of the population for further improvement. The approaches are referred as ANN-Suliman heuristic andANN-GAheuristic respectively. Makespan of the sequences obtained by these heuristics are compared with themakespan of the sequences obtained using the heuristic proposed by Nawaz, Enscore and Ham (NEH) and Suliman Heuristic initialized with Campbell, Dudek and Smith (CDS) heuristic called as CDS-Suliman approach. It is found that the ANN-GA and ANN-Suliman heuristic approaches perform better than NEH and CDS-Suliman heuristics for the problems considered. extcopyright Springer Science+Business Media LLC 2009.
About the journal
JournalData powered by TypesetJournal of Intelligent Manufacturing
PublisherData powered by TypesetSpringer Science and Business Media LLC
Open Access0