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Design and Implementation of a Multi-Agent System for the Job Shop Scheduling Problem
Leila Asadzadeh, Kamran Zamanifar
Pages - 287 - 297     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
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KEYWORDS
Job Shop Scheduling Problem, Genetic Algorithms, Parallel Genetic Algorithms
ABSTRACT
Job shop scheduling is one of the strongly NP-complete combinatorial optimization problems. Developing effective search methods is always an important and valuable work. Meta-heuristic methods such as genetic algorithms are widely applied to find optimal or near-optimal solutions for the job shop scheduling problem. Parallelizing genetic algorithms is one of the best approaches that can be used to enhance the performance of these algorithms. In this paper, we propose an agent-based parallel genetic algorithm for job shop scheduling problem. In our approach, initial population is created in an agent-based parallel way then an agent-based method is used to parallelize genetic algorithm. Experimental results showed that the proposed approach enhances the performance.
CITED BY (2)  
1 Bharathi, K., & Vijaylakshmi, C. Optimization for Flexible Job Shop Scheduling by Evolutionary Representation.
2 Krishnan, M., Karthikeyan, T., Chinnusamy, T. R., & Murugesan, A. (2013). Dynamic Scheduling of Flexible Manufacturing System Using Scatter Search Algorithm. International Journal of Emerging Technology and Advanced Engineering, 3(10), 329-341.
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Miss Leila Asadzadeh
- Iran
leila_asadzadeh_cs@yahoo.com
Mr. Kamran Zamanifar
- Iran