Supply chain scheduling optimization based on genetic particle swarm optimization algorithm

被引：0
|
作者：
Feng Xiong
Peisong Gong
P. Jin
J. F. Fan
机构：
[1] Zhongnan University of Economics and Law,School of Business Administration
[2] Zhongnan University of Economics and Law,IOPSE, Institute of Operation Management & System Engineering
[3] Zhongnan University of Economics and Law,School of Civil Engineering and Architecture
[4] Wuhan University of Technology,undefined
来源：
Cluster Computing | 2019年 / 22卷
关键词：
Scheduling optimization; Hybrid algorithm; Genetic algorithm; Particle swarm algorithm;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In order to optimize supply chain scheduling problem in mass customization mode, the mathematical programming of supply chain scheduling optimization problem is modelled. At the same time, model mapping is defined as a directed graph to facilitate the application of intelligent search algorithms. In addition, the features of genetic algorithm and particle swarm algorithm are introduced. Genetic algorithm has a strong global search capability, and particle swarm optimization algorithm has fast convergence speed. Therefore, the two algorithms are combined to construct a hybrid algorithm. Finally, the hybrid algorithm is used to solve the supply chain optimization scheduling problem model. Compared with other algorithms, the results show that the hybrid algorithm has better performance. The mathematical programming model used in this paper can be further extended and improved.
引用
收藏
页码：14767 / 14775
页数：8
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