Supply chain scheduling optimization based on genetic particle swarm optimization algorithm

被引：19

作者：

Xiong, Feng ^{[1
,2
]}

Gong, Peisong ^{[3
]}

Jin, P. ^{[3
]}

Fan, J. F. ^{[4
]}

机构：

[1] Zhongnan Univ Econ & Law, Sch Business Adm, Wuhan 430073, Hubei, Peoples R China

[2] Zhongnan Univ Econ & Law, Inst Operat Management & Syst Engn, IOPSE, Wuhan 430073, Hubei, Peoples R China

[3] Zhongnan Univ Econ & Law, Wuhan 430073, Hubei, Peoples R China

[4] Wuhan Univ Technol, Sch Civil Engn & Architecture, Wuhan 430070, Hubei, Peoples R China

来源：

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2019年 / 22卷 / Suppl 6期

关键词：

Scheduling optimization; Hybrid algorithm; Genetic algorithm; Particle swarm algorithm;

D O I：

10.1007/s10586-018-2400-z

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 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

页码：14767 / 14775

页数：9

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