• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.6
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• pp.47-52
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• 2012

DAS(Distribution Automation System) is equipped with several software applications such as service restoration, loss minimization, and protective relay coordination. The software applications of DAS are very sensitive to the amount of section load being carried by a particular section of distribution lines. Moreover, each software application requires a different parameter of the section load according to its purpose. Therefore, This paper proposes a new section load management method using real-time measurement data of the distribution lines. In order to provide accurate data to DAS applications, this method considers section loads in terms of the relationship of power versus time. In order to establish that the proposed method is feasible, a performance-testing simulator was developed, and case studies were conducted for a modified real distribution network.

• Korean Management Science Review
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• v.10 no.2
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• pp.27-42
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• 1993

Markov chain models can be used to predict the state of the system in the future. We extend the existing Markov chain models in two ways. For the stationary model, we propose a procedure that obtains the transition probabilities by appling the empirical Bayes method, in which the parameters of the prior distribution in the Bayes estimator are obtained on the collaternal micro data. For non-stationary model, we suggest a procedure that obtains a time-varying transition probabilities as a function of the exogenous variables. To illustrate the effectiveness of our extended models, the models are applied to the macro and micro time-series data generated from actual survey. Our stationary model yields reliable parameter values of the prior distribution. And our non-stationary model can predict the variable transition probabilities effectively.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.71-82
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• 2001

Using the computer simulation method, we invest19ate the probability distribution of maximum eigenvalue of pair-wise comparison matrix, which has been used as a parameter for measuring the consistency of responses in analytic hierarchy process (AHP). We show that the shape of the distribution of the maximum eigenvalue is different according to the dimension of the matrix. In addition, we cannot find any evidence that the distribution of the Consistency Index is a Normal distribution, which has been claimed in the Previous literature. Accordingly, we suggest using so called K-index calcu1ated based on the concept of cumulative distribution function lather than based on that of arithmetic mean because the probabilistic distribution cannot be assumed to be a Normal distribution. We interpret the simulation results by comparing them with the suggestion of Saaty[11]. Our results show that using Saaty＇s value could be too generous when the dimension of the matrix is 3 and strict over 4. Finally, we propose new criteria for measuring the response consistency in AHP.

• Korean Management Science Review
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• v.12 no.2
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• pp.105-127
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• 1995

This paper solves different machine-part group formation (MPGF) problems using genetic algorithms to demonstrate that it can be a new robust alternative to the conventional heuristic approaches for optimization problems. We first give an overview of genetic algorithms: Its principle, various considerations required for its implementation, and the method for setting up parameter values are explained. Then, we describe the MPGF problem which are critical to the successful operation of cellular manufacturing or flexible manufacturing systems. We concentrate on three models of the MPGF problems whose forms of the objective function and/or constraints are quite different from each other. Finally, numerical examples of each of the models descibed above are solved by using genetic algorithms. The result shows that the solutions derived by genetic algorithms are comparable to those obtained through problem-specific heuristic methods.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.1-6
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• 2003

The torque is an important measure that represents the performance of a particular engine. Furthermore the information of engine torque can be used as a primary feedback parameter in modem engine management system. In this paper, a methodology is proposed for torque estimation of SI-engine. Since the proposed method uses cylinder pressure sensor, the torque can be estimated in a simple manner. The indicated torque is estimated from the peak pressure and its location, and the load torque is observed by the state observer based on the estimated indicated torque. The proposed method is accurate and robust against the variations that affect the torque production such as spark timing, mass air flow and others. This torque estimation method may be an alternative solution to the use of engine torque maps in a modem torque-based engine management system.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.1
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• pp.97-108
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• 2001

Design and evaluation of AGV-based material handling systems are very complicated due to the randomness and the large number of variables involved Vehicle travel time is a key parameter for designing and evaluating AGV systems. Although loaded travel time is relatively easy to estimate, determination of empty vehicle travel time is difficult due to the inherent randomness of material handling systems. Most previous studies assume that the empty vehicle travel time is the same as the loaded travel time or assume very specific environments. This paper presents new vehicle travel time models for AGV-based material transport systems. The research effort is focused on the estimation of empty vehicle travel time under various vehicle dispatching policies. Simulation experiments are used to verify the proposed travel time models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.173-181
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• 1995

통계적 공정관리 분야가 점차 컴퓨터를 이용한 관리를 하게 되므로써 공정관리의 온라인화, 실시간화가 되고 있는 가운데, 기존의 관리도는 공정의 파라미터를 기지의 상수값으로 간주하므로 모형이 잘못 설정되었을 경우 상당히 많은 오보(false alarm)를 발생하게 된다. 이에 본 연구는 연속생산공정에서, 칼만필터를 적용하여 공정의 불확실한 파라미터와 모형의 오차에도 불구하고 공정의 변화를 보다 빠르고 정확하게 탐지할 수 있는 관리도를 설계하였다. 본 연구에서 설계한 관리도는 관축치들간에 시간적 종속성이 존재하는 경우에 있어서, 관측치들을 시계열모형으로 묘사를 하여, 파라미터(parameter)를 추정하고, 잔차를 얻어서 만든 잔차관리도로서, 실시간으로 생산공정을 관리하는 경우 효과적임을 보이기 위하여, 컴퓨터 시뮬레이션을 통해 ARL을 구하여 기존에 사용되고 있는 관리도와 수행도를 비교.평가하였다.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.93-113
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• 1997

In studying an EOQ-like inventory model for a manufacturing process, a number of findings were made. The system can "go out of control" resulting in a relatively minor problem state or "break-down". When the production system is in the minor problem statei produces a number of defective items. It is assumed that each defective piece requires rework cost and related operations. Once the machine breakdown takes place, the production system produces severely defective items that are completely unusable. Each completely unusuable item is immediately discarded and incurs handling cost, scrapped raw material cost and related operations. Two investment options in improving the production process are introduced : (1) reducing the probability of machine breakdown, breakdowns, and (2) simultaneously reducing the probability of machine breakdowns and setup costs. By assuming specific forms of investment cost function, the optimal investment policies are obtained explicitly. Finally, to better understand the model in this paper, the sensitivity of these solutions to changes in parameter values and numerical examples are provided.amples are provided.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.151-165
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• 1997

We propose a method for estimating the probability of perfect PM from successive failure times of a repairable system. The system under study is maintained preventively at periodic times, and it undergoes minimal repair at failure. We consider Brown-Proschan imperfect PM model in which the system is restored to a condition as good as new with probability P and is otherwise restored to its condition just prior to failure. We discuss the identifiability problem when the PM modes are not recorded. The expectation-maximization principle is employed to handle the incomplete data problem. We assume that the lifetime distribution belongs to a parametric family with increasing failure rate. For the two parameter Weibull lifetime distribution, we propose a specific algorithm for finding the maximum lifelihood estimates of the reliability parameters : the probability of perfect PM (P), as well as the distribution parameters. The estimation method will provide useful results for maintaining real systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.306-315
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• 1995

This paper presents a bin picking method using a slit beam laser in which a robot recognizes all of the unoccluded objects from the top of jumbled objects, and picks them up one by one. Once those unoccluded objects are removed, newly developed unoccluded objects underneath are recognized and the same process is continued until the bin gets empty. To recognize unoccluded objects, a new algorithm to link edges on slices which are generated by the orthogonally mounted laser on the xy table is proposed. The edges on slices are partitioned and classified using convex and concave function with a distance parameter. The edge types on the neighborhood slices are compared, then the hamming distances among identical kinds of edges are extracted as the features of fuzzy membership function. The sugeno fuzzy integration about features is used to determine linked edges. Finally, the pick-up sequence based on MaxMin theory is determined to cause minimal disturbance to the pile. This proposed method may provide a solution to the automation of part handling in manufacturing environments such as in punch press operation or part assembly.