Solving the Job-Shop Scheduling Problem Based on Cellular Genetic Algorithm

被引：0
|
作者：
Wen Mingyue [1 ]
Zhang Yi [1 ]
Hu Fangjun [1 ]
Liu Zheng [1 ]
机构：
[1] China Three Gorges Univ, Hubei Key Lab Hydroelect Machinery Design & Maint, Yichang, Peoples R China
来源：
ADVANCES IN MECHATRONICS AND CONTROL ENGINEERING II, PTS 1-3 | 2013年 / 433-435卷
关键词：
cellular genetic algorithm; job-shop scheduling; combinational optimization; neighbor structure;
D O I：
10.4028/www.scientific.net/AMM.433-435.639
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Cellular genetic algorithm (cGA) is a subclass of genetic algorithm (GA) in which the population diversity and exploration are enhanced thanks to the existence of small overlapped neighborhoods. Such a kind of structured algorithms is specially well suited for complex problems. Shop scheduling problem is a kind of problem with practical significance, and it belongs to a combinational optimization problem called NP-hard problem. In this paper we establish the model of job-shop problem(JSP) and solve the job-shop scheduling problem with cGA and traditional genetic algorithms(sGA). From the experimental results and analysis, we find cGA has better search efficiency and convergence performance than sGA.
引用
收藏
页码：639 / 644
页数：6
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