• IE interfaces
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• v.7 no.2
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• pp.75-85
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• 1994

This paper presents a production scheduling model in a block assembly shop in shipbuilding industry. In a block assembly shop, the most important performance criterion is load leveling, which balances manpower and work area utilization through the planning horizon. The problem is formulated as a mixed-integer nonlinear programming(MINLP) problem of which objective function is to optimize load leveling. The developed MINLP problem can not be solvable due to computational complexity. The MINLP problem is decomposed into two stage mixed-integer linear programming (MILP) problems to obtain a good solution, but the decomposed MILP problems are still computationally intractable because of combinatorial complexity. Therfore, a heuristic method using linear programming is proposed to solve two stage MILP problems sequentially. The proposed heuristic generates a good production schedule within a reasonable computation time, and it is easily applicable for establishing the production schedule in a block assembly shop in shipbuilding industry.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1826-1829
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• 2003

Fine chemical production must assure high-standard product quality as well as characterized as multi-product production in small volumes. Installing high-precision batch distillation is one of the common elements in the successful manufacturing of fine chemicals, and the importance of the process operation strategy with quality assurance cannot be overemphasized. In this study, we investigate the optimal operation strategy and production planning of a sequential multi-purpose plants consisting of batch processes and batch distillation with unlimited intermediate storage. We formulated this problem as an MILP model. A mixed-integer linear programming model is developed based on the time slot, which is used to determine the production sequence and the production path of each batch. Illustrative examples show the effectiveness of the approach.

• Journal of the korean society of oceanography
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• v.37 no.1
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• pp.45-50
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• 2002

New production (NP) values in well-mixed waters of the Yellow Sea were estimated using two different methods and were compared with each other; one is from the quantum yield model of nitrate uptake and chlorophyll ${\alpha}$-specific light absorption coefficient, and the other is from a traditional $^{15}N$-labelled stable isotope uptake technique. The quantum yields of nitrate uptake were highly variable, ranging from 0.0001 to 0.04 mol $NO_3Ein^{-1}$, and the small values in this study might have resulted from either the partitioning into nitrate uptake of little portions of light energy absorbed by phytoplankton or that phytoplankton may predominantly utilize other N sources (E. G. ammonium and/or urea) than nitrate. The estimates (0.54-8.47 nM $h^{-1}$) of NP from the quantum yield model correlated well ($r^2$=0.67, p<0.1) with those (0.01-4.93 nM $h^{-1}$) obtained using the $^{15}NO_3$ uptake technique. To improve the ability of estimating NP values using this model in the Yellow Sea, more data need to be accumulated in the future over a variety of time and space scales.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.589-605
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• 1996

Recycling of used products and components has been considered as one of promising strategies for resolving environmental problems. In this respect, most manufacturing companies begin to consider possible recycling (e.q., reuse or re-production) of the components contained in their products. The primary objective of this research is to develop a multiple criteria decision making model for systematic management of recycle-oriented manufacturing components. The production planning problem of recycle-oriented manufacturing components is first formulated as a multiobjective mixed 0-1 integer programming model with three conflicting objectives. An interactive multiple criteria decision making method is then developed for solving the mathematical model. Also, an Input/Output analysis software is developed to help practitioners apply the model to real problems without much knowledge on computers and mathematical programming. A numerical example is used in examining the validity of the proposed model and to investigate the impact of the input variables on recycling production strategy.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.11
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• pp.1521-1525
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• 2014

This study investigated heritability for bovine growth estimated with genomewide single nucleotide polymorphism (SNP) information obtained from a DNA microarray chip. Three hundred sixty seven Korean cattle were genotyped with the Illumina BovineSNP50 BeadChip, and 39,112 SNPs of 364 animals filtered by quality assurance were analyzed to estimate heritability of body weights at 6, 9, 12, 15, 18, 21, and 24 months of age. Restricted maximum likelihood estimate of heritability was obtained using covariance structure of genomic relationships among animals in a mixed model framework. Heritability estimates ranged from 0.58 to 0.76 for body weights at different ages. The heritability estimates using genomic information in this study were larger than those which had been estimated previously using pedigree information. The results revealed a trend that the heritability for body weight increased at a younger age (6 months). This suggests an early genetic evaluation for bovine growth using genomic information to increase genetic merits of animals.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.8
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• pp.1035-1039
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• 2000

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.51 no.1
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• pp.107-112
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• 2018

One-dimensional numerical model is implemented to investigate temperature variations in the mixed layer depth (MLD) with the passage of typhoons. In the model, we assume a non-divergent, infinite ocean and consider wind effects only, excluding isostatic effects (inverse barometric effects) and upwelling with vertical movement of the water column. Numerical experiments investigate the effects of typhoon tracks on temperature variations, including their dependence on vertical resolutions in the MLD and these results are compared with those in a three-dimensional primitive equation model (POM). The model reproduces features of the observed temperature variations in the MLD fairly well, and implies that wind effects, rather than isostatic effects, play a predominant role in temporal and spatial temperature variations in the MLD. After the passage of typhoons, however, the model does not reproduce well the temperature variations observed in the MLD, because a limitation of the model is its inability to reproduce events such as cyclonic eddy formation (Hong et al., 2011; Masuda and Hong, 2011). The model also shows well the so called 'rightward bias' (Price, 1981) of sea surface cooling which is the most predominant in the right hand side of typhoon's track.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.6
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• pp.642-647
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• 1998

A Poisson error model as a generalized linear mixed model (GLMM) has been suggested for genetic analysis of counted observations. One of the assumptions in this model is the normality for random effects. Since this assumption is not always appropriate, a more flexible model is needed. For count traits, a Poisson hierarchical generalized linear model (HGLM) that does not require the normality for random effects was proposed. In this paper, a Poisson-Gamma HGLM was examined along with corresponding analytical methods. While a difficulty arises with Poisson GLMM in making inferences to the expected values of observations, it can be avoided with the Poisson-Gamma HGLM. A numerical example with simulated embryo yield data is presented.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.713-718
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• 2016

Predicting precise specifications of differential mixed-signal circuits is a difficult problem, because analytically derived correlation between process variations and conventional specifications exhibits the limited prediction accuracy due to the phase unbalance, for most self-tests. This paper proposes an efficient prediction technique to provide accurate specifications of differential mixed-signal circuits in a system-on-chip (SoC) based on a nonlinear statistical nonlinear regression technique. A spectrally pure sinusoidal signal is applied to a differential DUT, and its output is fed into another differential DUT through a weighting circuitry in the loopback configuration. The weighting circuitry, which is employed from the previous work [3], efficiently produces different weights on the harmonics of the loopback responses, i.e., the signatures. The correlation models, which map the signatures to the conventional specifications, are built based on the statistical nonlinear regression technique, in order to predict accurate nonlinearities of individual DUTs. In production testing, once the efficient signatures are measured, and plugged into the obtained correlation models, the harmonic coefficients of DUTs are readily identified. This work provides a practical test solution to overcome the serious test issue of differential mixed-signal circuits; the low accuracy of analytically derived model is much lower by the errors from the unbalance. Hardware measurement results showed less than 1.0 dB of the prediction error, validating that this approach can be used as production test.

• Journal of Korean Society for Quality Management
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• v.16 no.1
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• pp.78-87
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• 1988

The purpose of this study is to develop the algorithm applicable to the integrated production inventory model under quantity discount. To achieve this purpose, the integrated production inventory model which unifies the inventory problem of raw materials and the finished product for a single product manufacturing system is considered. The product is manufactured in batches and the raw materials are obtained from outside suppliers but some of the raw materials are discounted according to the purchasing quantity. The intergrated production inventory problem considered in this study is formulated by the non-linear mixed integer programming model, and the optimal solution is obtained by using the algorithm developed by Goyal. Then, the algorithm developed by this study is applied to the quantity discount problem, and the optimal solution is revised by this results. The quantity discount algorithm of the integrated production inventory model developed by this study gives a systematic procedure to obtain the optimum policy to minimize the total cost in any case. The numerical example involving 20 raw materials and 5 raw materials among them are discounted according to the purchasing quantity is given to verify the mathematical model and the algorithm developed in this study.