A new genetic algorithm for flexible job-shop scheduling problems

被引：1
作者：
Imen Driss
Kinza Nadia Mouss
Assia Laggoun
机构：
[1] University of Batna,Automatics & Manufacturing Engineering Laboratory
来源：
Journal of Mechanical Science and Technology | 2015年 / 29卷
关键词：
FJSP; Scheduling; Genetic algorithm; Chromosome representation;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：1273 / 1281
页数：8
相关论文
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