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Simulation Optimization of Manufacturing System using Real-coded Genetic Algorithm  

Park, Kyoung-Jong (Dept. of Business Administration, Gwangju University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.28, no.3, 2005 , pp. 149-155 More about this Journal
Abstract
In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.
Keywords
Real-coded genetic algorithm; Simulation optimization; Binary-coded genetic algorithm;
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