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 mac hines. Approach: In this study Genetic Algorithm (GA) is integrated with the parallel version of Sim ulated Annealing Algorithm (SA) is applied to the job shop scheduling problem. The proposed algor ithm is implemented in a distributed environment using Remote Method Invocation concept. The new genetic operator and a parallel simulated annealing algorithm are developed for sol ving 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 prop osed system. Conclusion/Recommendations: The empirical results show that the proposed geneti c algorithm with simulated annealing is quite successful to achieve better solution than the indi vidual genetic or simulated annealing algorithm.
Highlights
Meta-heuristics is used to solve with the computationally hard optimization problems
After getting the best solution, client machine send it to specifies the problem instances, Column 2 specifies the number of jobs, Column 3 shows the number of machines, Column 4 specify the optimal value for each problem
The performance of the proposed HGAPSA algorithm is compared with the Genetic Algorithm
Summary
Meta-heuristics is used to solve with the computationally hard optimization problems. Metaheuristics consist of a high level algorithm that guides the search using other particular methods. Metaheuristics can be used as a standalone approach for solving hard combinatorial optimization problems. The standalone approach is drastically changed and attention of researchers has shifted to consider another type of high level algorithms, namely hybrid algorithms. There are at least two issues has to be considered while combining more than one metaheuristics: (a) how to choose the meta-heuristic methods and (b) how to combine the chosen heuristic methods into new hybrid approaches. There are no theoretical foundations for these issues For the former, different classes of search algorithms can be considered for the purposes of hybridization, such as exact methods, simple heuristic methods and metaheuristics.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
