A genetic algorithm and data mining based meta-heuristic for job shop scheduling problem

Author

Harrath, Youssef ; Chebel-Morello, Brigitte ; Zerhouni, Noureddine

Author_Institution

Lab. d´´Automatique de Besancon, CNRS, Besancon, France

Volume

7

fYear

2002

fDate

6-9 Oct. 2002

Abstract

Job shop scheduling (JSS) is a strongly NP-hard problem of combinatorial optimisation and one of the most well known machine scheduling problems. We propose a method based on a genetic algorithm and data mining to resolve this problem. The developed genetic algorithm generates a learning population of good solutions, which are mined by the mean of See5 classifier systems. The mining step produces decision rules which are transformed in to a meta-heuristic allowing the affectation of operations on machines.

Keywords

data mining; directed graphs; genetic algorithms; learning systems; pattern classification; production control; See5 classifier systems; combinatorial optimisation; data mining; decision rules; genetic algorithm; job shop scheduling problem; learning population; machine scheduling problem; meta-heuristic; strongly NP-hard problem; Data mining; Discrete wavelet transforms; Dispatching; Floods; Genetic algorithms; Job shop scheduling; Manufacturing processes; NP-complete problem; NP-hard problem; Simulated annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2002 IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-7437-1

Type

conf

DOI

10.1109/ICSMC.2002.1175709

Filename

1175709