Paper Info
Title
Scheduling fuzzy job shop using random key genetic algorithm
Abstract
Genetic algorithm has been successfully applied to fuzzy job shop scheduling problem, however, the coding and decoding strategies of the problem aren't fully investigated. This paper presents an efficient random key genetic algorithm (RKGA) for the problem to minimize the maximum fuzzy completion time. RKGA uses a novel random key representation, a new decoding strategy and discrete crossover. RKGA is applied to some fuzzy scheduling instances and compared with a genetic algorithm and particle swarm optimization with genetic operators. Computational results demonstrate that RKGA has the promising advantage on fuzzy scheduling.
Year
DOI
Venue
2010
10.1109/ICMLC.2010.5580535
ICMLC
Keywords
Field
DocType
fuzzy set theory,decoding strategy,random key representation,job shop scheduling,discrete crossover,fuzzy processing time,fuzzy job shop scheduling,genetic algorithm,genetic algorithms,maximum fuzzy completion time,genetic operator,particle swarm optimization,random key genetic algorithm,machine learning,cybernetics,decoding,schedules,genetics
Genetic operator,Mathematical optimization,Job shop scheduling,Crossover,Computer science,Job shop,Fuzzy logic,Flow shop scheduling,Fuzzy set,Artificial intelligence,Machine learning,Genetic algorithm
Conference
Volume
ISBN
Citations 
4
978-1-4244-6526-2
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
You-Lian Zheng1172.27
Yuanxiang Li224551.20
De-ming Lei317618.60
Chuanxiang Ma4152.85