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An effective genetic optimization approach for re-entrant flowshop problem
Sun Chengxia 1 * #,Guo He 2
1.Software School,Dalian University of Technology
2.Software School,Dalian University of technology
*Correspondence author
#Submitted by
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Funding: none
Opened online:12 October 2010
Accepted by: none
Citation: Sun Chengxia,Guo He.An effective genetic optimization approach for re-entrant flowshop problem[OL]. [12 October 2010] http://en.paper.edu.cn/en_releasepaper/content/4387405
 
 
In semiconductor manufacturing, the process of wafer fabrication is arguably the most technologically complex and capital intensive stage. This large-scale discrete-event process is highly re-entrant with hundreds of machines and processing steps. These optimization problems fall into the class of NP-hard problems. This paper addresses an optimized approach for the re-entrant flow shop scheduling problem (RFSP) by using genetic algorithm (GA). An effective mutation method with range restriction is proposed to minimize total turn around time (TTAT), especially dealing with the accidents effectively when re-scheduling is needed. Then we compare the modified genetic method with local search method and FIFO method. Experimental results indicated that the proposed method can efficiently balance the population variety and computing rate.
Keywords:genetic algorithm; flow shop scheduling; re-entrant
 
 
 

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