American Journal of Applied Sciences | |
Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling | Science Publications | |
Balasubramanie Palanisamy1  Thamilselvan Rakkiannan1  | |
关键词: Job shop scheduling; genetic algorithm; simulated annealing; | |
DOI : 10.3844/ajassp.2012.1694.1705 | |
学科分类:自然科学(综合) | |
来源: Science Publications | |
【 摘 要 】
Problem statement: The Job Shop Scheduling Problem (JSSP) is observed as one of the most difficult NP-hard, combinatorial problem. The problem consists of determining the most efficient schedule for jobs that are processed on several machines. Approach: In this study Genetic Algorithm (GA) is integrated with the parallel version of Simulated Annealing Algorithm (SA) is applied to the job shop scheduling problem. The proposed algorithm is implemented in a distributed environment using Remote Method Invocation concept. The new genetic operator and a parallel simulated annealing algorithm are developed for solving job shop scheduling. Results: The implementation is done successfully to examine the convergence and effectiveness of the proposed hybrid algorithm. The JSS problems tested with very well-known benchmark problems, which are considered to measure the quality of proposed system. Conclusion/Recommendations: The empirical results show that the proposed genetic algorithm with simulated annealing is quite successful to achieve better solution than the individual genetic or simulated annealing algorithm."
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO201911300611773ZK.pdf | 283KB | download |