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Job Scheduling with Genetic Algorithm

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dc.description.abstract In this paper, we have used a Genetic Algorithm (GA) approach for providing a solution to the Job Scheduling Problem (JSP) of placing 5000 jobs on 806 machines. The GA starts off with a randomly generated population of 100 chromosomes, each of which represents a random placement of jobs on machines. The population then goes through the process of reproduction, crossover and mutation to create a new population for the next generation until a predefined number of generations are reached. Since the performance of a GA depends on the parameters like population size, crossover rate and mutation rate, a series of experiments were conducted in order to identify the best parameter combination to achieve good solutions to the JSP by balancing makespan with the running time. We found that a crossover rate of 0.3, a mutation rate of 0.15 and a population size of 100 yield the best results. en_US
dc.title Job Scheduling with Genetic Algorithm en_US
dc.date.accessioned 2013-04-29T19:52:54Z
dc.date.available 2013-04-29T19:52:54Z
dc.date.issued 2013-04-29
dc.identifier.uri http://hdl.handle.net/10365/22775
dc.thesis.degree Paper (M.S.)--North Dakota State University, 2013. en_US
dc.contributor.advisor Ludwig, Simone
dc.subject.lcsh Production scheduling. en_US
dc.subject.lcsh Genetic algorithms. en_US
dc.creator.author Barat, Debarshi
dc.degree.departmentCollege Master of Science / Computer Science, College of Science and Mathematics, 2013.
dc.date.created 2013

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