Paper Info
Title
Scheduling jobs and preventive maintenance on fuzzy job shop using genetic algorithm
Abstract
Preventive maintenance (PM) has been considered on many scheduling problems, however, the problem of scheduling jobs and PM on fuzzy job shop are seldom investigated. This paper presents a random key genetic algorithm (RKGA) for the problem with resumable jobs and PM in the fixed time intervals. RKGA uses a novel random key representation, a new decoding strategy incorporating maintenance operation, and discrete crossover. RKGA is applied to some instances to minimize the maximum fuzzy completion time. Computational results show the optimization ability of RKGA on fuzzy scheduling with PM.
Year
DOI
Venue
2010
10.1109/ICMLC.2010.5580802
ICMLC
Keywords
Field
DocType
fuzzy set theory,decoding strategy,random key representation,fuzzy job shop scheduling,fixed time intervals,maintenance operation,preventive maintenance,discrete crossover,job shop scheduling,genetic algorithm,genetic algorithms,fuzzy scheduling,fuzzy job shop,random key,maximum fuzzy completion time,job scheduling,random key genetic algorithm,decoding,cybernetics,maintenance engineering,machine learning,scheduling problem
Computer science,Scheduling (computing),Job shop,Artificial intelligence,Genetic algorithm,Preventive maintenance,Distributed computing,Mathematical optimization,Job shop scheduling,Fuzzy logic,Flow shop scheduling,Job scheduler,Machine learning
Conference
Volume
ISBN
Citations 
3
978-1-4244-6526-2
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
You-Lian Zheng1172.27
Yuanxiang Li224551.20
De-ming Lei317618.60