• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.3
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• pp.413-422
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• 2000

In the class of models which include random effects, the variance component estimates are important to obtain accurate predictors and estimators. Variance component estimation is straightforward for balanced data but not for unbalanced data. Since orthogonality among factors is absent in unbalanced data, various methods for variance component estimation are available. REML estimation is the most widely used method in animal breeding because of its attractive statistical properties. Recently, Bayesian approach became feasible through Markov Chain Monte Carlo methods with increasingly powerful computers. Furthermore, advances in variance component estimation with complicated models such as generalized linear mixed models enabled animal breeders to analyze non-normal data.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.81-96
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• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1211-1218
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• 2008

Maximum likelihood estimator (MLE) and restricted maximum likelihood estimator (REMLE) approaches are available in analyzing the linear mixed model (LMM) like bioequivalence trials. US FDA (2001) guides that REMLE may be useful to assess bioequivalence (BE) test. This paper studies the statistical behaviors of the methods in assessing BE tests when some of observations are missing at random. The simulation results show that the REMLE maintains the given nominal level well and the MLE gives a bit higher power. Considering the levels and the powers, the REMLE approach is recommended when the sample sizes are small to moderate and the MLE approach should be used when the sample size is greater than 30.

• Coupled systems mechanics
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• v.12 no.6
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• pp.539-555
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• 2023

Although some prediction models have successfully developed for ultra-high performance concrete (UHPC), they do not provide insights and explicit relations between all constituents and its consistency, and compressive strength. In the present study, based on the experimental results, several mathematical models have been evaluated to predict the consistency and the 28-day compressive strength of UHPC. The models used were Linear, Logarithmic, Inverse, Power, Compound, Quadratic, Cubic, Mixed, Sinusoidal and Cosine equations. The applicability and accuracy of these models were investigated using experimental data, which were collected from literature. The comparisons between the models and the experimental results confirm that the majority of models give acceptable prediction with a high accuracy and trivial error rates, except Linear, Mixed, Sinusoidal and Cosine equations. The assessment of the models using numerical methods revealed that the Quadratic and Inverse equations based models provide the highest predictability of the compressive strength at 28 days and consistency, respectively. Hence, they can be used as a reliable tool in mixture design of the UHPC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.478-493
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• 2024

In this paper, the Mixed Integer Linear Programming (MILP) model is improved for searching differential characteristics of block cipher Midori-64, and 4 search strategies of differential path are given. By using strategy IV, set 1 S-box on the top of the distinguisher to be active, and set 3 S-boxes at the bottom to be active and the difference to be the same, then we obtain a 5-round differential characteristics. Based on the distinguisher, we attack 12-round Midori-64 with data and time complexities of 2⁶³ and 2^103.83, respectively. To our best knowledge, these results are superior to current ones.

• Korean Management Science Review
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• v.25 no.1
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• pp.93-106
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• 2008

This study is focused on the problem of seat allocation for group travel demand in airlines. We first explain the characteristic of group demand and its seat allocation process. The group demand in air travel markets can be classified into two types : incentive and package groups. Allocating seats for group demand depends on the types of group demand and the relationship between airlines and travel agents. In this paper we concentrate on the package group demand and develop an optimization model for seat allocation on the demand to maximize the total revenue. With some assumptions on the demand distribution and the linear approximation technique, we develop a mixed IP model for solving our problem optimally. From the computational experiments, we can find our optimization model can be applied well for real-world application.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.589-598
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• 2015

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.3
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• pp.420-424
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• 1987

The model reduction methods of the linear time invariant continuous systems are proposed. The energy dispersion method is used to obtain the model denominator. And the model numerator is found by the modified residue method or the time moment matching method. The methods suggested are compared with the method suggested by Lucas and give good results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1610-1616
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• 2013

Recently, district heating and cooling (DHC) systems based on combined cycle generation (CCG) providers are increasing in Korea. Since characteristics of combined heat and power (CHP) generators and heat demands of providers, heat trading between DHC providers based on the economic viewpoint is required; the heat trading has been doing. In this paper, a mathematical model for optimal operation based on heat trading between DHC providers is proposed. Especially, start-up characteristic of CCG is included. The operation model is established by mixed integer linear programming (MILP).