Genetic algorithm based multi-objective scheduling in a flow shop with batch processing machines

被引:2
作者
Lei, Deming [1 ]
Zhang, Qiongfang [1 ]
Cheng, Wen [1 ]
Wang, Tao [1 ]
Guo, Xiuping [2 ]
机构
[1] Wuhan Univ Technol, Univ Springfield, Sch Automat, Wuhan, Hubei, Peoples R China
[2] Southwest Jiaotong Univ, Sch Econ & Management, Dept Management Sci & Engn, Chengdu, Sichuan, Peoples R China
来源
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
关键词
Genetic algorithm; Flow shop; Batch processing machine; Multi-objective optimization; External archive; INCOMPATIBLE JOB FAMILIES; MINIMIZING MAKESPAN; SIZES;
D O I
10.1109/WCICA.2010.5554035
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the problem of minimizing makespan and the total tardiness in a flow shop with batch processing machines (BPM) is considered and an efficient genetic algorithm (GA) is presented, in which job permutation is the only optimization object and the solution of problem can be directly obtained using the permutation. To obtain a set of non-dominated solutions, a rank and the weighted objective based binary tournament selection and an external archive updating strategy are also adopted. The proposed GA is finally tested and the computational results show its promising performance on multi-objective scheduling of flow shop with BPM.
引用
收藏
页码:694 / 699
页数:6
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