MAS equipped with ant colony applied into dynamic job shop scheduling

被引：0
|
作者：
Kang, Kai [1 ]
Zhang, Ren Feng [1 ]
Yang, Yan qing [1 ]
机构：
[1] Hebei Univ Technol, Sch Management, Tianjin 300401, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2007年 / 4682卷
关键词：
dynamic scheduling; multi-agent system; ant colony optimization;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a methodology adopting the new structure of MAS(multi-agent system) equipped with ACO(ant colony optimization) algorithm for a better schedule in dynamic job shop. In consideration of the dynamic events in the job shop arriving indefinitely schedules are generated based on tasks with ant colony algorithm. Meanwhile, the global objective is taken into account for the best solution in the actual manufacturing environment. The methodology is tested on a simulated job shop to determine the impact with the new structure.
引用
收藏
页码：823 / 835
页数：13
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