• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.295-311
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• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• Journal of Family Resource Management and Policy Review
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• v.7 no.1
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• pp.153-167
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• 2003

The purposes of this study were to examine the pattern of housewives’social activity participation and investigate the characteristics of this pattern. The data of this study were collected from 340 housewives living in Seoul area. Statistical methods used were frequency, percentage, oneway ANOVA, DMR-test, and cluster analysis. The results of this study were as follows; 1) 52.6% of respondents participated in social education, only 20.8％ in volunteer work. And a large number of housewives(77.6% of respondents) wanted to get a job. 2) Housewives were classified into job-demand group, job-apathy group and social activity oriented group by the pattern of social activity participation. And these groups were differed by Age, income, degree of satisfaction as a housewife, time constraints, and money constraints.

• Structural Engineering and Mechanics
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• v.54 no.4
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• pp.725-740
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• 2015

This study presents critical buckling load optimization of the axially graded layered uniform columns. In the first place, characteristic equations for the critical buckling loads for all boundary conditions are obtained using the transfer matrix method. Then, for each case, square of this equation is taken as a fitness function together with constraints. Due to explicitly unavailable objective function for the critical buckling loads as a function of segment length and volume fraction of the materials, especially for the column structures with higher segment numbers, initially, prescribed value is assumed for it and then the design variables satisfying constraints are searched using Differential Evolution (DE) optimization method coupled with eigen-value routine. For constraint handling, Exterior Penalty Function formulation is adapted to the optimization cycle. Different boundary conditions are considered. The results reveal that maximum increments in the critical buckling loads are attained about 20% for cantilevered and pinned-pinned end conditions and 18% for clamped-clamped case. Finally, the strongest column structure configurations will be determined. The scientific and statistical results confirmed efficiency, reliability and robustness of the Differential Evolution optimization method and it can be used in the similar problems which especially include transcendental functions.

• Education of Primary School Mathematics
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• v.23 no.4
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• pp.157-173
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• 2020

Although there are various form of statistical graphs in the real world, the statistical graphs in elementary mathematics textbooks are very formalized by the pedagogical constraints. In this study, I examine the frames of statistical graphs and their educational importance, and analyze the frames in Korean, Australian, and MiC textbooks. As a result, the frames of statistical graphs in elementary mathematics textbooks (1) draws students' attention to the components of the graphs, (2) plays a supplementary role in students' drawing graphs by hands, and (3) helps to apply school mathematics to statistical problem solving in real life. The frames of statistical graphs in Korean textbooks is the form of tables focusing on (1) and (2), but these of MiC textbooks has various forms focusing on (3). On the other hand, Austalian textbooks introduced the table-form frames of statistical graphs at the lower graders, but gradually changed to the axis-form frames as the grade level increased. Based on this, a recommendation was drawn on how to deal with the frames of statistical graphs in elementary mathematics textbooks.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.189-199
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• 2020

This paper studies a Bayesian ordered multiple linear regression model with skew normal error. It is reasonable that the kind of inherent information available in an applied regression requires some constraints on the coefficients to be estimated. In addition, the assumption of normality of the errors is sometimes not appropriate in the real data. Therefore, to explain such situations more flexibly, we use the skew-normal distribution given by Sahu et al. (The Canadian Journal of Statistics, 31, 129-150, 2003) for error-terms including normal distribution. For Bayesian methodology, the Markov chain Monte Carlo method is employed to resolve complicated integration problems. Also, under the improper priors, the propriety of the associated posterior density is shown. Our Bayesian proposed model is applied to NZAPB's apple data. For model comparison between the skew normal error model and the normal error model, we use the Bayes factor and deviance information criterion given by Spiegelhalter et al. (Journal of the Royal Statistical Society Series B (Statistical Methodology), 64, 583-639, 2002). We also consider the problem of detecting an influential point concerning skewness using Bayes factors. Finally, concluding remarks are discussed.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.1-8
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• 2000

A spectrum minimizing the frequency-domain Kullback-Leibler information number has been proposed and used to modify a spectrum estimate. Some numerical examples have illustrated the KL spectrum estimate is superior to the initial estimate, i.e., the autocovariances obtained by the inverse Fourier transformation of the KL spectrum estimate are closer to the sample autocovariances of the given observations than those of the initial spectrum estimate. Also, it has been shown that a Gaussian autoregressive process associated with the KL spectrum is the closest in the timedomain Kullback-Leibler sense to a Gaussian white noise process subject to given autocovariance constraints. In this paper a corresponding conditional probability theorem is presented, which gives another rationale to the KL spectrum.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.143-156
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• 2006

In the present paper, various solutions for generalized canonical correlation analysis (GCCA) are considered depending on the criteria and constraints. For the comparisons of some characteristics of the solutions, we provide with certain unifying stationary equations which might to also useful to obtain various generalized canonical correlation analysis solutions. In addition, we suggest an approach for the generalized canonical correlation analysis by exploiting the concept of maximum eccentricity originally de-signed to test the internal independence structure. The solutions, including new one, are compared through unifying stationary equations and by using some numerical illustrations. A type of iterative procedure for the GCCA solutions is suggested and some numerical examples are provided to illustrate several GCCA methods.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.507-518
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• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.137-143
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• 2011

The classification of subjects with unknown distribution in a small sample size often involves order-restricted constraints in multivariate parameter setups. Those problems make the optimality of a conventional likelihood ratio based statistical inferences not feasible. Fortunately, Roy (1953) introduced union-intersection principle(UIP) which provides an alternative avenue. Multivariate linear rank statistics along with that principle, yield a considerably appropriate robust testing procedure. Furthermore, conditionally distribution-free test based upon exact permutation theory is used to generate p-values, even in a small sample. Applications of this method are illustrated in a real microarray data example (Lobenhofer et al., 2002).