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A Taguchi Approach to Parameter Setting in a Genetic Algorithm for General Job Shop Scheduling Problem  

Sun, Ji Ung (School of Industrial & Managment Engineering Hankuk University of Foreign Studies)
Publication Information
Industrial Engineering and Management Systems / v.6, no.2, 2007 , pp. 119-124 More about this Journal
Abstract
The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.
Keywords
Genetic Algorithm; Parameter Optimization; Taguchi Method; Robust Design; General Job Shop Scheduling;
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