TY  - JOUR
AU  - Rakkiannan, Thamilselvan 
AU  - Palanisamy, Balasubramanie 
PY  - 2012
TI  - Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling
JF  - American Journal of Applied Sciences
VL  - 9
IS  - 10
DO  - 10.3844/ajassp.2012.1694.1705
UR  - https://thescipub.com/abstract/ajassp.2012.1694.1705
AB  - 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.