An artificial neural network based heuristic for flow shop scheduling problems

Authors:
T. Radha Ramanan;R. Sridharan;Kulkarni Sarang Shashikant;A. Noorul Haq
Affiliations:
Department of Mechanical Engineering, National Institute of Technology Calicut, Calicut, India 673601;Department of Mechanical Engineering, National Institute of Technology Calicut, Calicut, India 673601;Department of Mechanical Engineering, National Institute of Technology Calicut, Calicut, India 673601;Department of Mechanical Engineering, National Institute of Technology Calicut, Calicut, India 673601
Venue:
Journal of Intelligent Manufacturing
Year:
2011

Citing 5
Cited 3

The application of the simulated annealing algorithm to the solution of the n/m/Cmax flowshop problem

Computers and Operations Research
A genetic algorithm for flowshop sequencing

Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms for flowshop scheduling problems

Computers and Industrial Engineering
A neural-net approach to real time flow-shop sequencing

Computers and Industrial Engineering
A neural network to enhance local search in the permutation flowshop

Computers and Industrial Engineering

Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems

Journal of Intelligent Manufacturing
Neural network based modeling and optimization of deep drawing --- extrusion combined process

Journal of Intelligent Manufacturing
Fast password recovery attack: application to APOP

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

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.