• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

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.

• IE interfaces
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• v.21 no.4
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• pp.444-455
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• 2008

In the present manufacturing environment, the appropriate decision making strategy has a significance and it should count on the fast-changing demand of customers. This research derives the optimal levels of the decision variables affecting the inventory related performance in multi-stage supply chain by using simulation and genetic algorithm. Simulation model helps analyze the customer service level of the supply chain computationally and the genetic algorithm searches the optimal solutions by interaction with the simulation model. Our experiments show that the integration approach of the genetic algorithm with a simulation model is effective in finding the solutions that achieve predefined target service levels.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.199-203
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• 1998

Process planning and scheduling are traditionally regarded as separate tasks performed sequentially. But if two tasks are performed concurrently, greater performance can be achieved. In this study, we propose new approach to integration of process planning and scheduling. We propose new process planning combinations selection method using simulation based genetic algorithms. Computational experiments show that proposed method yield better performance when compared with existing methods.

• Korean Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.215-221
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• 1997

A new approach in solving design centering problem is presented. Like most stochastic optimization problems, optimal design centering problems have intrinsic difficulties in multivariate intergration of probability density functions. In order to avoid to avoid those difficulties, genetic algorithm and very coarse Monte Carlo simulation are used in this research. The new algorithm performs robustly while producing improved yields. This result implies that the combination of robust optimization methods and approximated simulation schemes would give promising ways for many stochastic optimizations which are inappropriate for mathematical programming.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.4
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• pp.428-434
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• 1997

The effect of genetic diversity in sub-populations on breeding efficiency was examined with prospect of potential crossbreeding. Simulation study of selection was performed for 20 generations with 20 replications each, comparing average breeding values and inbreeding coefficients between the two breeding systemes; single population scheme and two population scheme. The different genetic levels were assumed to be caused by different gene frequencies. Phenotypes of two traits generated polygenic effect with additive 36 loci and residuals distributed normally were selected by selection index procedure. High genetic gain with less inbreeding was clearly recognized in the single population scheme, independently of difference in genetic level, economic weight and genetic correlation. Genetic correlation after selection in the single population scheme was lower than the two population scheme. When crossbreeding between the sub-population was taken into account, superiority of the two population scheme was suggested under those restrictions; difference in genetic level is moderate, selection criterion for the two traits is not far from even economic weight, and genetic correlation is positive with low to moderate value. The use of complementarity increased the possibility of the two population scheme.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.171-178
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• 2004

This study suggests a general paradigm enhancing genetic mutability. Mutability among heterogeneous members in a genetic population has been a major problem in application of genetic programming to diverse business problems. This suggested paradigm is implemented to developing new methods from existing methods. Within the evolutionary approach taken to designing new methods, a general representation scheme of the genetic programming framework, called a kernel, is introduced. The kernel is derived from the literature of algorithms and heuristics for combinatorial optimization problems. The commonality and differences among these methods have been identified and again combined by following the genetic inheritance merging them. The kernel was tested for selected methods in combinatorial optimization. It not only duplicates the methods in the literature, it also confirms that each of the possible solutions from the genetic mutation is in a valid form, a running program. This evolutionary method suggests diverse hybrid methods in the form of complete programs through evolutionary processes. It finally summarizes its findings from genetic simulation with insight.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.4
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• pp.422-427
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• 1997

Breeding efficiency was investigated to reveal crucial factors for constructing effective breeding system with subdivided populations under equal genetic level. Simulation study of selection experiment was performed for 20 generations with 20 replications each, comparing average breeding values and inbreeding coefficients between the two breeding systems; single population scheme and two population scheme, each of which had the same genetic parameters. Genetic correlations (-0.5 to 0.5) were assumed to be caused only by pleiotropic effect of a gene. Phenotypes of the two traits generated by polygenic effect with additive 36 loci and residuals distributed normally were selected by two traits selection index procedure. Comparing between the single population scheme and the two population scheme, the single population scheme showed higher genetic gain with lower inbreeding coefficient. This result was confirmed particularly for the situation of high selection intensity, high heritability and high degree of unevenness for economic weight. Genetic correlations in the single population scheme were significantly lower than the two population scheme when initial genetic correlation was negative. When terminal crossbreeding for the two population scheme is taken into account, superiority of the two population scheme was suggested. The terminal crossbreeding was effective under the situation of long term selection, existence of moderate inbreeding depression and use of less extreme economic weight.

• Bulletin of the Korean Chemical Society
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• v.19 no.5
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• pp.544-548
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• 1998

A Monte Carlo simulation incorporated with the genetic algorithm is presented to describe the defect known as "transition from Y-to X-type deposition" of the cadmium arachidate Langmuir-Blodgett multilayer film. Simulation is performed based on the detachment models of XY-type deposition. The transition is simulated by introducing a probability of surface molecule detachment considering interaction between neighboring molecules. The genetic algorithm is incorporated into Monte Carlo simulation to get the optimum value of the probability factors. The distribution of layers having different thickness predicted by the simulation correlates well with the measured distribution of thickness using the small-angle X-ray reflectivity. The effect of chain length and subphase temperature on the detachment probability are investigated using the simulation. Simulation results show that an increase (or a decrease) of two hydrocarbon chain is roughly equivalent to the detachment probability to a temperature decrease (or increase) of 15 K.

• Journal of Power System Engineering
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• v.17 no.5
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• pp.86-93
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• 2013

This paper proposes the used of genetic algorithm for optimizing PI controller and describes the dynamic modeling simulation for the permanent magnet synchronous motor driven by simplified vector control with the aid of MATLAB-Simulink environment. Furthermore, three kinds of error criterion minimization, integral absolute error, integral square error, and integral time absolute error, are used as objective function in the genetic algorithm. The modeling procedures and simulation results are described and presented in this paper. Computer simulation results indicate that the genetic algorithm was able to optimize the PI controller and gives good control performance of the system. Moreover, simplified vector control on permanent magnet synchronous motor does not need to regulate the direct axis component current. This makes simplified vector control of the permanent magnet synchronous motor very useful for some special applications that need simple control structure and low cost performance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.100-101
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• 2004

[ $\bigcirc$ ] In the growth simulation using genetic coefficients calculated with fooled data under various condition, WAGT was not higher and LAI, WLVG, WSO were higher, but WST was similar before grain-filling stage after the became lower because of higher translocation of carbohydrates than in the growth simulation using genetic coefficients calculated with data under high nitrogen applicated condition. $\bigcirc$ Genetic coefficients should be calculated with data showing potential in ORYZA2000, but under 180 kg and 240 kg N condition in 2003, plants were infected by panicle blast and also yield was not higher than under 120 kg N condition showing not potential condition and therefore not appropriate for genetic coefficients estimation compared with pooled data from various condition.