A new genetic algorithm for flexible job-shop scheduling problems

被引：69

作者：

Driss, Imen ^{[1
]}

Mouss, Kinza Nadia ^{[1
]}

Laggoun, Assia ^{[1
]}

机构：

[1] Univ Batna, Automat & Mfg Engn Lab, Batna, Algeria

来源：

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | 2015年 / 29卷 / 03期

关键词：

F[!text type='JS']JS[!/text]P; Scheduling; Genetic algorithm; Chromosome representation; OPTIMIZATION;

D O I：

10.1007/s12206-015-0242-7

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.

引用

收藏

页码：1273 / 1281

页数：9

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