Solving fuzzy flexible job shop scheduling problems using genetic algorithm

被引：7
|
作者：
Lei, De-Ming [1 ]
Guo, Xiu-Ping [2 ]
机构：
[1] Wuhan Univ Technol, Sch Automat, Wuhan, Hubei Province, Peoples R China
[2] Southern Jiaotong Univ, Sch Econ & Management, Chengdu, Sichuan, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年
关键词：
fuzzy processing time; flexible job shop scheduling; genetic algorithm;
D O I：
10.1109/ICMLC.2008.4620553
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a two-population genetic algorithm (TPGA) for FfJSSPs with the maximum fuzzy completion time. TPGA uses two-string representation to represent a solution and two populations to search the optimal schedule. In each generation, crossover and mutation are only applied to one part of the chromosome and these populations are combined and updated by using half of the individuals with the bigger fitness in the combined population. Some instances of FfJSSP are designed and the performance of TPGA is tested. The computational results demonstrate the promising performance of TPGA on FfJSSP.
引用
收藏
页码：1014 / +
页数：4
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