Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling
- 1 Faculty of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode 638 052, Tamilnadu, India
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.
Copyright: © 2012 Thamilselvan Rakkiannan and Balasubramanie Palanisamy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Job shop scheduling
- genetic algorithm
- simulated annealing