Warehouse Optimization Model Based on Genetic Algorithm

被引：4

作者：

Qin, Guofeng ^{[1
]}

Li, Jia ^{[2
,3
]}

Jiang, Nan ^{[1
]}

Li, Qiyan ^{[1
]}

Wang, Lisheng ^{[1
]}

机构：

[1] Tongji Univ, Comp Sci & Technol Dept, Shanghai 200092, Peoples R China

[2] Shanghai Bao Steel Logist Co Ltd, Shanghai 200940, Peoples R China

[3] Northeastern Univ, Sch Business Adm, Shenyang 110819, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2013年 / 2013卷

关键词：

Genetic algorithms;

D O I：

10.1155/2013/619029

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.

引用

页数：6