Date of Award

2013

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Ludwig, Simone

Description

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.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/22775

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