A hybrid neural network- meta heuristics approach for permutation flow shop scheduling problems

Author

Ramanan, T Radha ; Kulkarni, Subhash S. ; Sridharan, R.

Author_Institution

Dept. of Mech. Eng., Nat. Inst. of Technol. Calicut, Calicut, India

fYear

2010

fDate

7-10 Dec. 2010

Firstpage

286

Lastpage

290

Abstract

The objective of this study is to find a sequence of jobs for the permutation flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the 10 machine problem taken from the literature. The network is trained with the optimal sequences for five, six and seven jobs problem. This trained network is then used to solve the problem with greater number of jobs. The sequence obtained using neural network is used to generate initial population for genetic algorithm (ANN-GA), genetic algorithm using Random Insertion Perturbation Scheme (ANN-GA-RIPS) and Simulated Annealing (ANN-SA). Makespans obtained through these approaches are compared with the Taillard´s benchmark problems.

Keywords

backpropagation; feedforward neural nets; flow shop scheduling; genetic algorithms; job shop scheduling; simulated annealing; back propagation neural network; feed forward neural network; genetic algorithm; job sequence; machine problem; meta heuristics approach; permutation flow shop scheduling; random insertion perturbation; simulated annealing; Artificial neural networks; Job shop scheduling; Optimal scheduling; Processor scheduling; Simulated annealing; Upper bound; Flow Shop Scheduling; Genetic Algorithm; Neural Networks; Random Insertion Perturbation Scheme; Simulated annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on

Conference_Location

Macao

ISSN

2157-3611

Print_ISBN

978-1-4244-8501-7

Electronic_ISBN

2157-3611

Type

conf

DOI

10.1109/IEEM.2010.5674470

Filename

5674470