Security based bi-objective flow shop scheduling model and its hybrid genetic algorithm

被引：5
|
作者：
Qing-Dao-er-Ji, Ren [1 ]
Wang, Yuping [2 ]
机构：
[1] Inner Mongolia Univ Technol, Sch Informat Engn, Hohhot 010051, Peoples R China
[2] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 243卷
基金：
中国国家自然科学基金;
关键词：
Multi-objective flow shop scheduling; Genetic algorithm; Crossover operator; Mutation operator; Local search; PARTICLE SWARM OPTIMIZATION; SEQUENCE-DEPENDENT SETUP; LOCAL SEARCH; TIME; MAKESPAN; MINIMIZE;
D O I：
10.1016/j.amc.2014.06.019
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we considered the flow shop scheduling problem with respect to the both objectives of the makespan and the mean continuous running time, proposed a security based bi-objective flow shop scheduling model. To solve the proposed model more effectively, we presented a hybrid genetic algorithm (HGA), which used some tailor made genetic operators and a local search operator in order to improve the local search ability of GA. The proposed algorithm is tested with some well-known problems in literature. The computational results demonstrated the effectiveness of the proposed algorithm. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码：637 / 643
页数：7
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