• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.10 no.16
• /
• pp.107-112
• /
• 1987

This paper is to present the problem of ＱC technique usage and improvement alternative in the ＱC circle. First, in the selection theme, it must have easy relations of tangible/intangible effects through simple theme's title. And contents development most be consistency in tangible/intangible effects. Second, in the usage of ＱC technique, it is necessary to strengthen QC circle activity through ＱC circle education. So in the aspects of long-term period. Internal instruction is strengthened and, in the aspects of short-term period, internal evaluation is established.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.10a
• /
• pp.303-306
• /
• 1996

This paper proposes a heuristic method for job shop scheduling with alternative machines. Our heuristic suggests four machine-selecting rules and two priority dispatching rules for modifying existent ones considering alternative machines, and then it extends existing nondelay/active job shop schedule generation. This heuristic provides good criteria(rules) in the selection of a proper machine among those performing a specific operation and for the dispatch of an operation to a selected machine and thus these rules permit the efficient job shop scheduling with alternative machines. The performances of our four machine-selecting rules in addition to the two priority dispatching rules, applied together with the existing 17 rules, are experimented and evaluated, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.20 no.43
• /
• pp.127-137
• /
• 1997

This paper proposes a heuristic method for job shop scheduling with alternative machines. Our heuristic suggests two machine-selecting rules and two priority dispatching rules for modifying existent ones considering alternative machines, and then it extends existing nondelay/active job shop schedule generation. This heuristic provides good criteria(rules) in the selection of a proper machine among those performing a specific operation and for the dispatch of an operation to a selected machine and thus these rules permit the efficient job shop scheduling with alternative machines. The performances of our two machine-selecting rules in addition to the two priority dispatching rules, applied together with the existing 17 rules, are experimented and evaluated, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.32 no.3
• /
• pp.142-158
• /
• 2009

The supplier selection problem is perhaps the most important component of the purchasing function. Some of the common and influential criteria in the selection of a supplier include quality, price, delivery, and service. These evaluation criteria often conflict, however, and it is frequently impossible to find a supplier that excels in all areas. In addition, some of the criteria are quantitative and some are qualitative. Thus, a methodology is needed that can capture both subjective and objective evaluation measures. The Analytic Hierarchy Process(AHP) is a decision-making method for ranking alternative courses of action when multiple criteria must be considered. This paper proposes the AHP-based approach which can structure the supplier selection process and the achievements-based procedure which can allocate order quantities for the selected suppliers In automotive part manufacturers. Also, through the practical case of 'D' automotive part manufacturing company, we shows that the proposed AHP based supplier selection approach and the achievements-based allocation procedure of order quantity can be successfully applied for supplier selection and order quantity allocation problems.

• Journal of the Korea Safety Management & Science
• /
• v.17 no.4
• /
• pp.287-295
• /
• 2015

modern logistics are required to carry out functions such as timely adjustment and swift adaptation to changing patterns, and this leads to the emphasis on forming logistics parks. Logistics parks make profits using the efficiency of time and space. Such logistics parks play an important role in a corporation creating operating profits as well as acting as a method of alternative investment for individuals. Logistics parks no longer simply store materials, but have become a place that plays an important role in various areas of corporate and individual activities, and thus the analysis of the selection of the location of logistics parks and the related characteristics is extremely important. There are many existing studies on the selection of locations of logistics parks but work on the factors related to location selection by industry seem lacking. As such, in the course of this study we have used preceeding studies to draft a questionnaire on which selection factors affect the selection of logistics park location in different industries and conducted empirical analysis of the questionnaire results to uncover the factors that affect the selection of the locations of logistics parks in different industries.

• The Korean Journal of Applied Statistics
• /
• v.26 no.5
• /
• pp.821-834
• /
• 2013

Law et al. (2004) proposed a normal distribution based salient mixture model for variable selection in clustering. However, this model has substantial problems such as the unidentifiability of components an the inaccurate selection of informative variables in the case of a small cluster size. We propose an alternative method to overcome problems and demonstrate a good performance through experiments on simulated data and real data.

• Asia pacific journal of information systems
• /
• v.21 no.2
• /
• pp.43-58
• /
• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.9
• /
• pp.11-20
• /
• 2015

In order to prevent the elderly people from committing suicide attempts, it is necessary to verify attributes that affect the suicide attempts. It is noted that previous studies have focused on qualitative approaches, and simple correlation analyses to determine the attributes related to the suicide attempts in the elderly people. However, such previous approaches had led to insufficient performance when facing with complicated data sets. In this sense, this study suggests an alternative method in which attribute selection techniques are adopted to determine more relevant attributes of the suicide attempts occurring in Korean elderly people. To verify empirical validity of our proposed method, we used Korea National Health and Nutrition Examination Survey (KNHANES) from January 2007 to December 2012. Empirical results proved that the proposed attribute selection techniques showed better predictive effectiveness; 94.4% compared to the simple statistical methods. This study proposes a way to determining the elderly suicide and preventing it to happen.

• Communications for Statistical Applications and Methods
• /
• v.24 no.6
• /
• pp.651-662
• /
• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

• Communications for Statistical Applications and Methods
• /
• v.25 no.6
• /
• pp.591-604
• /
• 2018

The accelerated failure time (AFT) model is a linear model under the log-transformation of survival time that has been introduced as a useful alternative to the proportional hazards (PH) model. In this paper we propose variable-selection procedures of fixed effects in a parametric AFT model using penalized likelihood approaches. We use three popular penalty functions, least absolute shrinkage and selection operator (LASSO), adaptive LASSO and smoothly clipped absolute deviation (SCAD). With these procedures we can select important variables and estimate the fixed effects at the same time. The performance of the proposed method is evaluated using simulation studies, including the investigation of impact of misspecifying the assumed distribution. The proposed method is illustrated with a primary biliary cirrhosis (PBC) data set.