Hybrid Taguchi-Based Particle Swarm Optimization for Flowshop Scheduling Problem

被引：7
|
作者：
Yang, Ching-I [1 ]
Chou, Jyh-Horng [1 ,2 ]
Chang, Ching-Kao [1 ]
机构：
[1] Natl Kaohsiung First Univ Sci & Technol, Inst Engn Sci & Technol, Kaohsiung 807, Taiwan
[2] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, Kaohsiung 807, Taiwan
来源：
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 2014年 / 39卷 / 03期
关键词：
Flowshop scheduling problem; Dynamic weight; Taguchi-based crossover; Fuzzy inference system; GENETIC LOCAL SEARCH; ALGORITHM; DESIGN;
D O I：
10.1007/s13369-013-0756-1
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
A hybrid Taguchi-based particle swarm optimization (HTPSO) method is developed for solving multi-objective flowshop scheduling problems (FSPs). Search performance is improved using Taguchi-based crossover to avoid scheduling conflicts. Instead of the conventional approach to selecting dynamic weights randomly, which ignores very small weight values for the objective, a fuzzy inference system is used. A numerical example is given to demonstrate the application of the proposed HTPSO and its good performance. The numerical results show that the HTPSO effectively enhances particle swarm optimization. The improvement achieved by the HTPSO also exceeds that obtained by existing methods for finding Pareto optimum solutions for FSPs. Therefore, the proposed HTPSO method effectively solves multi-objective FSPs.
引用
收藏
页码：2393 / 2412
页数：20
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