• Journal of Korean Society for Quality Management
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• v.36 no.3
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• pp.34-44
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• 2008

This research proposes a design method based on the statistical characteristics of the Shewhart control chart incorporated with 2 of 2 and 2 of 3 runs rules respectively. A Markov chain approach is employed in order to calculate the in-control and out-of-control average run lengths(ARL). Two different control limit coefficients for the Shewhart scheme and the runs rule scheme are derived simultaneously to minimize the out-of-control average run length subject to the reasonable in-control average run length. Numerical examples show that the statistical performance of the hybrid control scheme are superior to that of the original Shewhart control chart.

• Journal of Korean Society for Quality Management
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• v.40 no.2
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• pp.176-185
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• 2012

After a work of Derman and Ross(1997) that considered simple main runs rules and derived ARL (Average Run Length) using Markov chain modeling, $\bar{X}$ control chart based on diverse alternative main and supplementary runs rules that is the most popular control chart for monitoring the mean of a process are proposed. This paper reviews and discusses the-state-of-art researches for these runs rules and classifies according to several properties of runs rules. ARL derivation for a proposed runs rule is also illustrated.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.467-480
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• 2012

Purpose: This paper introduces new 2-of-3 main and supplementary runs rules to increase the performance of the classical $\bar{X}$ control chart for detecting small process shifts. Methods: The proposed runs rules are compared with other competitive runs rules by numerical experiments. Nonlinear optimization problem to minimize the out-of-control ARL at a specified shift of process mean for determining action and warning limits at a time is formulated and a procedure to find two limits is illustrated with a numerical example. Results: The proposed 2-of-3 main and supplementary runs rules demonstrate an improved performance over other runs rules in detecting a sudden shift of process mean by simultaneous changes of mean and standard deviation. Conclusion: To increase the performance in the detection of small to moderate shifts, the proposed runs rules will be used with $\bar{X}$ control charts.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.308-317
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• 2008

This research investigates economic characteristics of 2 of 2 runs rules under the Shewhart $\bar{X}$ control chart scheme. A Markov chain approach is employed in order to calculate the in-control average run length (ARL) and the average length of analysis cycle. States of the process are defined according to the process conditions at sampling time and transition probabilities are derived from the state definitions. A steady state cost function is constructed based on the Lorezen and Vance(1986) model. Numerical examples show that 2 of 2 runs rules are economically superior to the Shewhart $\bar{X}$ chart in many cases.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.184-188
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• 1999

The performance of an AGV varies with the applied AGV dispatching rule in the operation of AGVS. This study proposes a multi-attribute AGV dispatching rule. The suggested dispatching rule considers the output queue of a workstation, distance between an idle AGV and a workstation to be served, the input queue of the destination and the remaining job process of a part. This study suggests two types of and the remaining job process of a part. This study suggests two types of multi-attribute dispatching rules. One is an one-stage rule which selects the part to be served considering four attributes simultaneously. The other is a two-stage rule by which a workstation is selected and a part is chosen from the selected workstation. The simulation runs were executed under different experimental conditions to obtain preliminary statistics on the several performance measures.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.77-89
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• 2000

This paper suggests a multi-attribute dispatching rule for an automated guided vehicle(AGV). The attributes to be considered are the number of queues in outgoing buffers of workstations, distance between an idle AGV and a workstation with a job waiting for the service of vehicle, and the number of queues in input buffers of the destination workstation of a job. The suggested rule is based on the simple additive weighting method using a normalized score for each attribute. A neural network approach is applied to obtain an appropriate weight vector of attributes based on the current status of the manufacturing system. Backpropagation algorithm is used to train the neural network model. The proposed dispatching rules and some single attribute rules are compared and analyzed by simulation technique. A number of simulation runs are executed under different experimental conditions to compare the several performance measures of the suggested rules and some existing single attribute dispatching rules each other.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.459-473
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• 2004

It has well known that an exhaustive search algorithm suggested by Breiman et. a1.(1984) has a trend to select the variable having relatively many possible splits as an splitting rule. We propose an algorithm to overcome this variable selection bias problem and then construct unbiased regression trees based on the algorithm. The proposed algorithm runs two steps of selecting a split variable and determining a split rule for binary split based on the split variable. Simulation studies were performed to compare the proposed algorithm with Breiman et a1.(1984)'s CART(Classification and Regression Tree) in terms of degree of variable selection bias, variable selection power, and MSE(Mean Squared Error). Also, we illustrate the proposed algorithm with real data sets.

• Journal of Korean Society for Quality Management
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• v.22 no.1
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• pp.82-95
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• 1994

When independent individual measurements are taken both $S/c_4$ and $\bar{R}/d_2$ are unbiased estimators of the process standard deviation. However, with dependent data $\bar{R}/d_2$ is not an unbiased estimator of the process standard deviation. On the other hand $S/c_4$ is an asymptotic unbiased estimator. If there exists correlation in the data, positive(negative) correlation tends to increase(decrease) the ARL. The effect of using $\bar{R}/d_2$ is greater than $S/c_4$ if the assumption of independence is invalid. Supplementary runs rule shortens the ARL of X control charts dramatically in the presence of correlation in the data.

• Journal of Korean Society of Transportation
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• v.8 no.1
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• pp.5-15
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• 1990

As a result of the lack of methodology for the determination of navigational channel capacity and the consequence lack of effective management of traffic, navigational channels are often grossly underutilized or highly congested. The traditional rule of first-come-first-served admission of vessels to channels is not efficient as it assumes equal time intervals between entrance of consecutive vessels. A new vessel traffic management system is developed in this research and methodologies to measure the improvement in the channel capacity are developed. Methodology to measure the channel performances for three queue disciplines are developed. The effects of changes in major factors on the channel capacity model such as channel length, fleet mix and arrival rate, as well as changes in strategy are analyzed. Under given channel conditions, best strategy are recommended. Also, a method for effective stochastic channel capacity simulation was developed. The results of analysis and as ertions are compared with the results of simulation runs to prove their applicability.

• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.1-9
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• 2008

Proposed in the paper is an automated procedure to construct a plant model for PLC simulation. Since PLC programs only contain the control logic without the information on the plant model, it is necessary to build the corresponding plant model to perform simulation. Conventionally, a plant model for PLC simulation has been constructed manually, and it requires much efforts as well as the in-depth knowledge of simulation. As a remedy for the problem, we propose an automated procedure to generate a plant model from the symbol table of a PLC program. To do so, we propose a naming rule for PLC symbols so that the symbol names include enough information on the plant model. By analyzing such symbol names, we extract a plant model automatically. The proposed methodology has been implemented, and test runs were made.