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Title |
Solving Job Shop Scheduling Problem with Genetic Algorithm using Cloud Computing Approach |
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Author |
Saeed teimoori |
| Citation |
Vol. 25 No. 9 pp. 159-167 |
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Abstract |
In this research, we introduced a map-reduce framework from cloud computing to execute the genetic algorithms with large population sizes and overcome limited resource problem of common single computers. In comparison with related works such as MRPGA and Huang’s method. Huang and Park, using the model presented in, an algorithm for job shop scheduling problem solving is presented in the context of cloud computing. In this algorithm, the encryption method used based on start times of jobs and another field as the generation of genetic algorithm is defined in display individuals of the population. The algorithm MRPGA in which the claim is made that the current map-reduce model due to a fall in local optimization does not support as well as a genetic algorithm. So this method has tried to do changes in map-reduce architecture, So that the possibility of falling into the local optimum reduced. By comparing the proposed method with the MRPGA method and the standard Map-Reduce method, we concluded that the proposed algorithm performs better for different tests in terms of finding the exact results and thus produces shorter schedules. |
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Keywords |
Job shop scheduling problem, MRPGA method, Huang and park method |
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