Paper Info
Title
Research on flexible job-shop scheduling problem under uncertainty based on genetic algorithm
Abstract
In this paper, an improved genetic algorithm for optimization of flexible job-shop scheduling problem with fuzzy processing time and fuzzy due date is presented, which is used to research the complexities and essences of this problem. Firstly, the optimization model under uncertainty environment is built, and the objectives are to maximize the average agreement index, and minimize the maximum of fuzzy completion time and the workload of machine. Then the paper discusses some kinds of different situation and definition of fuzzy processing time and due date, gives their graphic description as well. After that, an improved genetic algorithm is presented to optimize the flexible job-shop scheduling problem under uncertainty. The feasibility of the optimization model and the improved genetic algorithm are validated through some instances.
Year
DOI
Venue
2010
10.1109/ICNC.2010.5583493
ICNC
Keywords
Field
DocType
fuzzy set theory,fuzzy completion time,optimization model,average agreement index,fuzzy processing time,job shop scheduling,genetic algorithm,genetic algorithms,fuzzy due date,flexible job-shop scheduling problem
Mathematical optimization,Job shop scheduling,Fair-share scheduling,Fuzzy set operations,Computer science,Flow shop scheduling,Nurse scheduling problem,Genetic algorithm scheduling,Artificial intelligence,Rate-monotonic scheduling,Dynamic priority scheduling,Machine learning
Conference
Volume
ISBN
Citations 
5
978-1-4244-5958-2
1
PageRank 
References 
Authors
0.35
4
4
Name
Order
Citations
PageRank
Jie Liu11419116.47
Chaoyong Zhang232023.22
Liang Gao317621.99
Xiao-Juan Wang4228.34