An artificial neural network based heuristic for flow shop scheduling problems |
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Authors: | T Radha Ramanan R Sridharan Kulkarni Sarang Shashikant A Noorul Haq |
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Affiliation: | (1) Department of Industrial Engineering and Management Information, Huafan University, Taipei, Taiwan, Republic of China |
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Abstract: | 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 and ANN-GA heuristic respectively. Makespan of the sequences obtained by these heuristics
are compared with the makespan 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. |
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Keywords: | |
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