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An artificial neural network based heuristic for flow shop scheduling problems
- Source :
- Journal of Intelligent Manufacturing. 22:279-288
- Publication Year :
- 2009
- Publisher :
- Springer Science and Business Media LLC, 2009.
-
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.
- Subjects :
- Mathematical optimization
education.field_of_study
Job shop scheduling
Artificial neural network
Heuristic
Computer science
business.industry
Population
Flow shop scheduling
Industrial and Manufacturing Engineering
Artificial Intelligence
Genetic algorithm
Null-move heuristic
Artificial intelligence
business
education
Heuristics
Software
Subjects
Details
- ISSN :
- 15728145 and 09565515
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- Journal of Intelligent Manufacturing
- Accession number :
- edsair.doi...........8e2b528082baf5452cc61b0b3f3ba11b