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The objective of this paper is to find a sequence of jobs for the permutation flow shop to minimise the makespan. The shop consists of 10 machines. A feed-forward back-propagation artificial neural network (ANN) is used to solve the problem. The network is trained with the optimal sequences for five-, six- and seven-job problems. This trained network is then used to solve a problem with a greater number of jobs. The sequence obtained using the neural network is used to generate the initial population for the genetic algorithm (GA) using the random insertion perturbation scheme (RIPS). The makespan of the sequence obtained by this approach (ANN-GA-RIPS) is compared with that obtained using GA starting with a random population (ANN-GA). It was found that the ANN-GA-RIPS approach performs better than ANN-GA starting with a random population. The results obtained are compared with those obtained using the Nawaz, Enscore and Ham (NEH) heuristic and upper bounds of Taillard's benchmark problems. ANN-GA-RIPS performs better than the NEH heuristic and the results are found to be within 5% of the upper bounds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper considers the sequencing of jobs that arrive in a flow shop in different combinations over time. Artificial neural network (ANN) uses its acquired sequencing knowledge in making the future sequencing decisions. The paper focuses on scheduling for a flow shop with ‘m’ machines and ‘n’ jobs. The authors have used the heuristics proposed by Campbell et al.(1970, A heuristic algorithm for n-jobs m-machines sequencing problem) to find a sequence and makespan (MS). Then a pair wise interchange of jobs is made to find the optimal MS and total flow time (TFT). The obtained sequence is used for giving training to the neural network and a matrix called neural network master matrix (NNMM) is constructed, which is the basic knowledge of the neurons obtained after training. From the matrix, interpretations are made to determine the optimum sequence for the jobs that arrive in the future over a period of time. The results obtained by the ANN are compared with a constructive heuristics and an improvement heuristics. The results show that the quality of the measure of performance is better when ANN approach is used than obtained by constructive or improvement heuristics. It is found that the system’s efficiency (i.e., obtaining the optimal MS and TFT) increases with increasing numbers of training exemplars. [ABSTRACT FROM AUTHOR]