• Journal of the Ergonomics Society of Korea
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• v.15 no.2
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• pp.113-124
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• 1996

This study was initiated to suggest the optimal size intervals for mail-order clothing. The questionnaire survey was carried at 360 women and 50 men who purchased apparels by mail-order. The garment sizes providing at the various mail-order companies in Korea were compared. The garment sizes that consumers wanted to purchase were also investigated. The collected data were analyzed and optimal size intervals for mail-order clothing were decided by the loss function. The results are as follow. 1) The optimal size intervals were varied from 4cm to 7cm. The total expected loss of the apparel sizes suggested in this study was less than that of the current mail-order apparel sizes. When the number of sizes for mail-order clothing is increased, the expected loss was reduced considerably. 2) The mail-order clothing is made for consumers with average body size. 30 The number of garment sizes available by mail-order was under three. 4) Subjects tend to select larger size garments, when the right size garments are not available.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.527-538
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• 2019

This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.207-224
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• 2000

Generally cluster size is predetermined when we use the stratified two-stage cluster sampling But in case that the sizes of clusters vary greatly one may want to make the sizes to be about equal. In this paper we study the optimal cluster size in stratified twostage cluster sampling. Also we find the optimal primary sampling unit sizes and optimal secondary sampling unit sizes under the given cost restriction.

• Journal of the military operations research society of Korea
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• v.12 no.1
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• pp.78-86
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• 1986

This paper analyzes how a shipowner or charterer may determine the specification of optimal ship size for a given route with respect to certain market requirements. The theory of optimal ship size, a methodology for estimating scale economics, and the various factors affecting ship size are examined using a typical conventional cargo ship and bulk cargo carriers based on shipowners' cost data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.330-333
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• 2003

Initial cluster size for clustering of partitioning methods is very important to the clustering result. In K-means algorithm, the result of cluster analysis becomes different with optimal cluster size K. Usually, the initial cluster size is determined by prior and subjective information. Sometimes this may not be optimal. Now, more objective method is needed to solve this problem. In our research, we propose a hybrid genetic algorithm, a tree induction based evolution algorithm, for determination of optimal cluster size. Initial population of this algorithm is determined by the number of terminal nodes of tree induction. From the initial population based on decision tree, our optimal cluster size is generated. The fitness function of ours is defined an inverse of dissimilarity measure. And the bagging approach is used for saying computational time cost.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.12-17
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• 2003

Optimal determination of cluster size has an effect on the result of clustering. In K-means algorithm, the difference of clustering performance is large by initial K. But the initial cluster size is determined by prior knowledge or subjectivity in most clustering process. This subjective determination may not be optimal. In this Paper, the genetic algorithm based optimal determination approach of cluster size is proposed for automatic determination of cluster size and performance upgrading of its result. The initial population based on attribution is generated for searching optimal cluster size. The fitness value is defined the inverse of dissimilarity summation. So this is converged to upgraded total performance. The mutation operation is used for local minima problem. Finally, the re-sampling of bootstrapping is used for computational time cost.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.245-251
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• 2004

Determination the optimal containership size is the most important factor for competitiveness if shipping companies. Accordingly, the objective if this research is determining the optimal containership size by service routes. Total shipping cost is calculated at the ground if capital cost, vessel operation costs, voyage costs, port charge and miscellaneous cost for 'Europe-Far East', 'Far East-North America' and 'Europe-Far East-North America' Services. Finally, the optimal containership size was utilized through total shipping cost, slot quantity if containership and average throughput by containership.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.75-84
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• 1994

In this paper we address the issue of how an optimal sample size computation relates the level of precision required for travel demand estimations and transportation planning. We approach the problem by 1) deriving a theoretical solution, 2) developing a computational procedure (algorithm) to implement the theoretical solution, and 3) demonstrating a practical application. Ultimately, we construct a formal scheme of optimal sample size computation for use in travel data collection processe.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.2
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• pp.3-17
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• 1994

This paper presents an EMQ model which determines an optimal manufacturing lot size in an unreliable machine. The machine has an exponentially distributed life time, and requires a constant time to repair when it fails. We 1) derive an average cost function, 2) obtain an optimal lot size and show that it is unique, 3) carry out sensitivity analysis to examine the effect of machine parameters (life time, time to repair) to optimal lot size.

• Journal of Korean Society for Quality Management
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• v.15 no.1
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• pp.76-81
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• 1987

Past treatment of the single machine maintenance problem has shown that preventive maintenance may be desirable for equipment for which failures are caused at least partially by wear-out factors. In all previous treatment, however, the size of the maintenance-repair crew has been held constant and the optimal maintenance period has then been determined. This paper suggests a simultaneous solution for the maintenance-repair crew size and the optimal maintenance period. The optimal maintenance period is seen to shift as the size of the maintenance-repair crew varies.