@article {10.3844/ajassp.2012.1694.1705,
article_type = {journal},
title = {Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling},
author = {Rakkiannan, Thamilselvan and Palanisamy, Balasubramanie},
volume = {9},
year = {2012},
month = {Aug},
pages = {1694-1705},
doi = {10.3844/ajassp.2012.1694.1705},
url = {https://thescipub.com/abstract/ajassp.2012.1694.1705},
abstract = {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.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}