• Industrial Engineering and Management Systems
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• 제4권2호
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• pp.165-175
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• 2005

Two extreme and one mixed procedures for designing a noise hazard prevention program are discussed in this paper. The two extreme design procedures (engineering-based and HPD-based) yield upper and lower bounds of the total noise control cost, respectively; while the mixed design procedure provides an optimal noise hazard prevention program within a given total budget. The upper bound of the workforce size for job rotation is approximated using a heuristic procedure. Six optimization models are developed and utilized by the mixed procedure to eliminate or reduce excessive noise levels (or noise exposures) in an industrial workplace. The mixed procedure also follows the OSHA’s hierarchy of noise control. A numerical example is given to demonstrate the application of the proposed design procedures.

• 한국경영과학회지
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• 제14권1호
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• pp.16-26
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• 1989

The terminal layout problem is fundamental in may centralized computer networks, which is generated formulated as the capaciated minimum spanning tree problem (CMSTP). We present an implementation of the dual-based procedure to solve the CMSTP. Dual ascent procedure generates a good feasible solutions to the dual of the linear programming relaxation of CMSTP. A feasible primal solution to CMSTP can then be constructed based on this dual solution. This procedure can be used either as a stand-alone heuristic or, else, it can be incorporated into a branch and bound algorithm. A numerical result is given with quite favorable results.

• 품질경영학회지
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• 제32권1호
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• pp.144-154
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• 2004

The bounded adjustment is known to be more efficient than repeated adjustment when the cost is incurred for engineering process control. The procedure of the bounded adjustment is to adjust the process when the one-step predicted deviation exceeds the adjustment limit by the amount of the prediction. In this paper, two run rules are proposed and studied In order to improve the efficiency of the traditional bounded adjustment procedure. The efficiency is studied in terms of the standardized cost through Monte Carlo simulation when the procedure is operated with and without the run rules. The adjustment procedure operated with run rules turns out to be more robust for changes in the process and cost parameters.

• 품질경영학회지
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• 제34권1호
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• pp.73-77
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• 2006

In this paper, the Bayesian procedure for the multiple test of fraction nonconforming, p, is proposed. It is the procedure for checking whether the process is out of control, in control, or under the permissible level for p. The procedure is as follows: first, setting up three types of models, $M_1:p=p_0,\;M_2:pp_0$, second, computing the posterior probability of each model. and then choosing the model with the largest posterior probability as a model most fitted for the observed sample among three competitive models. Finally, the simulation study is performed to examine the proposed method.

• 품질경영학회지
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• 제24권3호
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• pp.50-64
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• 1996

This article is concerned with procedures for detecting multiple y outliers in linear regression. The outward-testing procedure, which is controled by the initial subset and the minimum residuals, is suggested by two phases. The performance of this procedure is compared with others by Monte Carlo techniques and found to be superior. The procedure, however, fails in detecting y outliers that are on high-leverage cases in Phase 1. Thus, we proposed ELMS algorithm for a set of suspect observations, in Phase 1. In Phase 2, the proposed testing is conducted using the studentized residuals to see which of the suspect cases are outliers. Several examples are analyzed.

• 경영과학
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• 제18권2호
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• pp.25-38
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• 2001

The well-known back-propagation algorithm for multi-layered neural network has successfully been applied to pattern c1assification problems with remarkable flexibility. Recently. the multi-layered neural network is used as a powerful data mining tool. Nevertheless, in many cases with complex boundary of classification, the successful learning is not guaranteed and the problems of long learning time and local minimum attraction restrict the field application. In this paper, an Improved learning procedure of multi-layered neural network is proposed. The procedure is based on the generalized delta rule but it is particular in the point that the architecture of network is not fixed but enlarged during learning. That is, the number of hidden nodes or hidden layers are increased to help finding the classification boundary and such procedure is controlled by entropy evaluation. The learning speed and the pattern classification performance are analyzed and compared with the back-propagation algorithm.

• 한국경영과학회지
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• 제19권1호
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• pp.69-83
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• 1994

This paper deals with a capacitated ring-star network design problem (CRSNDP) with node capacity constraints. The CRSNDP is formulated as a mixed 0-1 integer problem, and a 2-phase heuristic solution procedure, ADD & VAM and RING, is developed, in which the CRSNDP is decomposed into two subproblems : the capacitated facility location problem (CFLP) and the traveling sales man problem (TSP). To solve the CFLP in phase I the ADD & VAM procedure selects hub nodes and their appropriate capacity from a candidate set and then assigns them user nodes under node capacity constraints. In phase II the RING procedure solves the TSP to interconnect the selected hubs to form a ring. Finally a solution of the CRSNDP can be achieved through combining two solution of phase I & II, thus a final design of the capacitated ring-star network is determined. The analysis of computational results on various random problems has shown that the 2-phase heuristic procedure produces a solution very fast even with large-scale problems.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2004년도 품질경영모델을 통한 가치 창출
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• pp.413-418
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• 2004

The bounded adjustment is known to be more efficient than repeated adjustment when the cost is incurred for engineering process control. The procedure of the bounded adjustment is to adjust the process when the one-step predicted deviation exceeds the adjustment limit by the amount of the prediction. In this paper, two run rules are proposed and studied in order to improve the efficiency of the traditional bounded adjustment procedure. The efficiency is studied in terms of the standardized cost through Monte Carlo simulation when the procedure is operated with and without the run rules. The adjustment procedure operated with run rules turns out to be more robust for changes in the process and cost parameters. The Markov chain approach for calculating the properties of the run rules is also studied.

• 품질경영학회지
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• 제26권1호
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 춘계학술대회
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• pp.325-329
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• 2006

In this paper, the Bayesian procedure for the multiple test of fraction nonconforming, p, is proposed. It is the procedure for checking whether the process is out of control, in control, or under the permissible level for p. The procedure is as follows: first, setting up three types of models, $M_1:p=p_0,\;M_2:pp_0$, second, computing the posterior probability of each model. and then choosing the model with the largest posterior probability as a model most fitted for the observed sample among three competitive models. Finally, the simulation study is performed to examine the proposed method.