A Genetics Algorithm for Solving Job-Shop Scheduling Problems in FMS

被引：0

作者：

Li, Shoutao ^{[1
,2
]}

Jiang, Wei ^{[1
]}

Tian, Wei ^{[1
]}

机构：

[1] Jilin Univ, Coll Commun Engn, Changchun 130025, Peoples R China

[2] Changchun Architecture & Civilengn Coll, Changchun 130604, Peoples R China

来源：

2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2015年

关键词：

Scheduling; Job shop; Genetic algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Job shop scheduling problem (Job shop Scheduling Problem) is one of the hardest combinatorial optimization problems. Due to its nonlinear characteristic and NP hard, the traditional algorithm cannot solve this problem. This paper proposes a genetic algorithm to solve the classic job shop machine scheduling problems and emphasizes on the genetic algorithms coding so as to present a coding mode based on the relationship between machines and different working procedures. On this point, this paper aimed at designing a new crossover and mutation genetic operators to perform a global search. Furthermore, the experiments are conducted and the results show that this method has high efficiency in solving the large scale job shop problem.

引用

页码：1634 / 1639

页数：6

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