• 경영과학
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• 제4권
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• pp.60-75
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• 1987

This paper introduces the outlines of the state-of-the-art in studying FMSs using analytical queueing network models. These include Jackson networks, reversible networks and approximate models of non-product-form networks. the focus is on identifying the major features of models as they relate to the operational characteristics of FMSs. Prescriptive models concerning the optimal design and/or operational control of FMS networks are also discussed. We notice that the presentation of materials in this paper basically follows Yao and Buzacott, On Queueing Network Models of Flexible Manufacturing Systems(FMSs), invited and published on Queueing Systems, Theory and Application 1(1986). For other analytical models of FMSs, refer to Buzacott and Yao, Flexible Manufacturing Systems : A Review of Analytical Models, Management Science 32, No.7(1986).

• 품질경영학회지
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• 제39권2호
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• pp.320-328
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• 2011

This research proposes comprehensive models for analyzing common cause failures (CCF) due to cumulative shocks and to assess system reliability under the CCF. The proposed cumulative shock models are based on the binomial failure rate (BFR) model. Six kinds of models are proposed so as to explain diverse cumulative shock phenomena. The models are composed of the initial failure probability, shape parameter, and the total shock number. Some parameters of the proposed models can not be explicitly estimated, so we adopt the Expectation-maximization (EM) algorithm in order to obtain the maximum likelihood estimator (MLE) for the parameters. By estimating the parameters for the cumulative shock models, the system reliability with CCF can be assessed sequentially according to the number of cumulative shocks. The result can be utilizes in dynamic probabilistic safety assessment (PSA), aging studies, or risk management for nuclear power plants. Replacement or maintenance policies can also be developed based on the proposed model.

• Management Science and Financial Engineering
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• 제16권3호
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• pp.49-70
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• 2010

Customer population management models can be classified into three categories: the first category includes the models that analyze the customer population at cohort level; the second one deals with the customer population at aggregate level; the third one has interest in the interactions among the customer populations in the competitive market. Our study proposes a model that can analyze the dynamics of customer population in consumer-durables market at aggregate level. The dynamics of customer population includes the retention curves from the purchase or at a specific duration time, the duration time expectancy at a specific duration time, and customer population growth or decline including net replacement rate, intrinsic rate of increase, and the generation time of customer population. For this study, we adopt mathematical ecology models, redefine them, and restructure interdisciplinary models to analyze the dynamics of customer population at aggregate level. We use the data of previous research on dynamic customer population management at cohort level to compare its results with those of ours and to demonstrate the useful analytical effects which the precious research cannot provide for marketers.

• Industrial Engineering and Management Systems
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• 제12권1호
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• pp.9-15
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• 2013

An inverse minimum spanning tree problem makes the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. In this paper, the concept of uncertain ${\alpha}$-minimum spanning tree is initiated for minimum spanning tree problem with uncertain edge weights. Using different decision criteria, two uncertain programming models are presented to formulate a specific inverse minimum spanning tree problem with uncertain edge weights involving a sum-type model and a minimax-type model. By means of the operational law of independent uncertain variables, the two uncertain programming models are transformed to their equivalent deterministic models which can be solved by classic optimization methods. Finally, some numerical examples on a traffic network reconstruction problem are put forward to illustrate the effectiveness of the proposed models.

• 전기학회논문지
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• 제65권12호
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• pp.2075-2083
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• 2016

The real time IAQ (Indoor Air Quality) management is very important for large buildings and underground facilities such as subways because poor IAQ is immediately harmful to human health. Such IAQ management requires monitoring, prediction and control in an integrated and real time manner. In this paper, we present three PM10 hourly prediction models for such realtime IAQ management as both Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. Both MLR and ANN models show good performances between 0.76 and 0.88 with respect to R (correlation coefficient) between the measured and predicted values, but the MLR models outperform the corresponding ANN models with respect to RMSE (root mean square error).

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2000년도 추계학술대회 및 정기총회
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• pp.103-106
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• 2000

As the industrial environment becomes more competitive, supply chain management (SCM) has become recognized as a major strategy in the business world. Some of current researches are categorized into review papers, deterministic models, stochastic models, simulation models and discussed in this paper. A hybrid approach combining analytic model and simulation model and the simulation optimization method are proposed as future research areas with other analytical subjects.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.65-69
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• 2003

As complex mathematical models are increasingly adopted for business decision-making, difficulties arise in reusing solvers (i.e., model solving algorithms) against diverse models and data sets and thus the collaboration among users (model/solver builders and decision makers) in multiple departments becomes very difficult. To facilitate the solver reuse, this paper adopts the Web services technologies as the base technologies for linking the solvers to the models, both of which are created on different modeling paradigms and different system platforms, in unified system architecture. Specifically, this paper focuses on designing an ontology that represents the interfacing semantics of the model-solver interactions in a general and standardized form. By referring to the ontology, a model management system (MMS) can autonomously suggest a set of compatible solvers and apply them to individual models even though the decision makers are not knowledgeable enough about all the details of the models and the solvers. Thus, this Web services based MMS would improve the reusability of the solvers by relieving the decision makers from the risk of erroneous application of a solver to syntactically and semantically incompatible models and the burden of considerable understanding of model and solver semantics.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.1181-1186
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• 2005

In many construction projects, progress and efficiency are hampered by poor communication of discipline-specific models. For example, architects use 2D or 3D CAD models and builders use CPM diagrams, Gantt charts, and spreadsheets to show their view of the project. Nowadays, advanced computer visualization tools, 4D CAD or VR, can show these disparate models to understand cross-disciplinary impacts of design and construction decisions. In Korea, several leading companies have tried to apply these tools to their pilot projects from the design phase to the maintenance phase. These companies have expected that more project stakeholders could understand a construction schedule more quickly and completely with 4D visualization than with the traditional construction management tools. However, modeling of the 4D CAD or VR can be quite time-consuming and expensive to generate manually and has therefore limited the spread and use of these models. In order to adopt widely those models in construction industry, the areas that those tools could support to take large benefits in diverse functional areas of construction management need to be analyzed. In this study, researchers analyze the usefulness and limitations of the 4D models and VR in the construction industry, develop the strategy of application priority, and improve the 4D modeling method.

• 품질경영학회지
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• 제42권2호
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• pp.221-234
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• 2014

Purpose: The Purpose of this study is to develop a model for predicting agent churn group in the cosmetics industry. We develope two models, pattern model and matrix model, which are compared regarding the prediction accuracy of churn agents. Finally, we try to conclude if there is statistically significant difference between two models by empirical study. Methods: We develop two models using the part of RFM(Recency, Frequency, Monetary) method which is one of customer segmentation method in traditional CRM study. In order to ensure which model can predict churn agents more precisely between two models, we used CRM data of cosmetics company A in China. Results: Pattern model and matrix model have been developed. we find out that there is statistically significant differences between two models regarding the prediction accuracy. Conclusion: Pattern model and matrix model predict churn agents. Although pattern model employed the trend of monetary mount for six months, matrix model that used the amount of sales per month and the duration of the employment is better than pattern model in prediction accuracy.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2009년도 춘계학술대회
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• pp.185-192
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• 2009

The research contributes extending and reviewing of restricted (constrained) and unrestricted (unconstrained) models in GLM(Generalized Linear Models). The paper includes the methodology for finding EMS(Expected Mean Square) and $F_0$ ratio. The results can be applied to the gauge R&R(Reproducibility and Repeatability) in MSA(Measurement System Analysis).