A genetic algorithm for flexible job shop scheduling with fuzzy processing time

被引：133
|
作者：
Lei, Deming [1 ]
机构：
[1] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Hubei, Peoples R China
来源：
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | 2010年 / 48卷 / 10期
关键词：
artificial intelligence; scheduling; evolutionary algorithms; OPTIMIZATION; HYBRID; DUEDATE;
D O I：
10.1080/00207540902814348
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper presents a flexible job shop scheduling problem with fuzzy processing time. An efficient decomposition-integration genetic algorithm (DIGA) is developed for the problem to minimise the maximum fuzzy completion time. DIGA uses a two-string representation, an effective decoding method and a main population. In each generation, DIGA decomposes the chromosomes of the main population into a job sequencing part and a machine assigning part and independently evolves the populations of these parts. Some instances are designed and DIGA is tested and compared with other algorithms. Computational results show the effectiveness of DIGA.
引用
收藏
页码：2995 / 3013
页数：19
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