A New Genetic Algorithms Combined with Learning Strategy for Flexible Job-Shop Scheduling Problem

被引：0

作者：

Xie, Shi-man ^{[1
]}

机构：

[1] Hebei Polytech Univ, Coll Mech Engn, Tangshan, Peoples R China

来源：

ADVANCED TECHNOLOGY IN TEACHING - PROCEEDINGS OF THE 2009 3RD INTERNATIONAL CONFERENCE ON TEACHING AND COMPUTATIONAL SCIENCE (WTCS 2009), VOL 2: EDUCATION, PSYCHOLOGY AND COMPUTER SCIENCE | 2012年 / 117卷

关键词：

Job-Shop Scheduling; Genetic Algorithms; Learning Strategy; 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：

In this paper, we have proposed a new method based on genetic algorithms and the learning by partial injection of sequences for solving the Flexible Job-shop Scheduling Problem (FJSP). Computational experiments show that the AGAIS (II) algorithm outperforms the performance of the AGAIS (I). In fact, the AGAIS (II) gives better solutions than AGAIS (I) in a reasonable computation time.

引用

页码：153 / 161

页数：9

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