대체연료 충전을 위한 기반시설의 효율적 설계 및 개발은 신에너지 경제의 촉진을 위해 필요하다. 본 논문은 GIS와 입지모델을 결합하여 공간의사결정지원 시스템(SDSS)의 원형을 개발하였다. 사용된 입지 모델은 일회 주유에 따른 주행거리가 제한적인 대체연료 차량을 대상으로 최대의 충전 서비스를 제공할 수 있는 지점에 충전소를 입지하며, 이때 운전자는 주유를 위해 기-종점 간의 최적경로에서 벗어나 우회할 수도 있다고 가정한다. 개발된 SDSS는 확장가능할 뿐 아니라 의사결정자가 다양한 수요 시나리오를 탐색할 수 있다. 이를 통해 대체연료차 운전자의 우회 행태, 시장 수요의 공간적 분포, 주행거리, 기존 시설물에 관한 상이한 가설을 세우고 그 결과를 실험할 수 있다. 주어진 조건을 만족하는 최선의 충전소 위치에 관한 결과는 지도 및 통계와 같은 다양한 산출물로 보고된다.

The quick response(QR) system is very popular in Korean apparel companies. However, the usage of QR system was not known well. The purpose of this study is to identify the usage of the quick response decision support system(QR DSS) and postponement manufacturing in the Korean apparel company. The researched company was the only one which used the QR DSS. The researchers carried out the depth interview with the QR decision makers of the company. This company had 14 brands, and had used the QR DSS since January, 2008. The results are as follows: The QR DSS was supportive computer software program, and it helped the staffs to make agile decision about QR repeat production of clothing. The QR DSS automatically calculated the related data, and suggested the expected sales volume and the proper supply amounts of the styles. There were four functions in QR DSS : 'QR Alert', 'Proper Supply Amount Simulation', 'Sensible QR', and 'Supply/Sales Simulation by Item'. The men's clothing brands effectively used 'Supply/Sales Simulation by Item' function. And the women's clothing brands effectively used 'QR Alert' function. This company also used the postponement production system for QR repeat production. The postponement production was conducted with four methods : the yarn stocking, the grey fabric stocking, the dyed fabric stocking, and the fabric sourcing. The men's clothing brands usually used of the yarn stocking methods and the dyed fabric stocking methods. The women's clothing brands usually used the grey fabric stocking methods. By using QR DSS and postponement production system the company was able to shorten the lead time for QR decision making.

The bullwhip effect is known as the significant factor which causes unnecessary inventory, lost sales or cost increase in supply chains. Therefore, the causes of the bullwhip effect must be examined and removed. In this paper, we develop two analytical to

  Supply Chain Management (SCM) system is a critical investment that can affect the competitiveness and performance of a company. Selection of a right SCM system is one of the critical issues. This paper presents the characteristic factors of SCM system a

  In order to implement Artificial Intelligence, various technologies have been widely used. Artificial Intelligence are applied for many industrial products and machine tools are the center of manufacturing devices in intelligent manufacturing devices. T

This paper is intended to develop a Bayesian decision model for the repair of deteriorating system. A non-homogeneous Poisson process with a power law failure intensity function is used to describe the behavior of the deteriorating repairable system. The decision on whether to have minimal repair or imperfect repair should be made on the occurrence of a failure. However, it is difficult to make a reasonable decision due to many uncertainties intrinsic in repair actions. In this paper, prior distributions are used in order to analyze the uncertainties embedded in the decision alternatives. Especially, a prior distribution for imperfect repair with probabilistic reduction in the failure intensity is proposed. In addition, mathematical expressions to calculate the expected prior loss of each repair alternative are proposed.

Supply Chain Management(SCM) system is a critical investment that can affect future competitiveness and performance of a company. Selection of a right SCM system is one of the critical issues. This paper provides the characteristic factors of SCM system selection and the SCM system evaluation and selection model based on Analytic Hierarchy Process(AHP). The proposed model can systematically construct the objectives of SCM system selection to support the business goals. A empirical example demonstrates the feasibility of the proposed model and the model can help a company to make better decision-making in selecting SCM system.

In order to implement Artificial Intelligence, various technologies have been widely used. Artificial Intelligence are applied for many industrial products and machine tools are the center of manufacturing devices in intelligent manufacturing devices. The purpose of this paper is to present the design of Decision Support Agent that is applicable to machine tools. This system is that decision whether to act in accordance with machine status is support system. It communicates with other active agents such as sensory and dialogue agent. The proposed design of decision support agent facilitates the effective operation and control of machine tools and provides a systematic way to integrate the expert's knowledge that will implement Intelligent Machine Tools.

This paper is intended to develop a Bayesian decision model for the repair of deteriorating system. A non-homogeneous Poisson process with a power law failure intensity function is used to describe the behavior of the deteriorating repairable system. The decision on whether to have minimal repair or imperfect repair should be made on the occurrence of a failure. However, it is difficult to make a reasonable decision due to many uncertainties intrinsic in repair actions. In this paper, prior distributions are used in order to analyze the uncertainties embedded in the decision alternatives. Especially, a prior distribution for imperfect repair with probabilistic reduction in the failure intensity is proposed. In addition, mathematical expressions to calculate the expected prior loss of each repair alternative are proposed.

As quality becomes a primary leading factor of organizational success, various management strategies have been In-troduced to Improve quality competitiveness Quality competitiveness, however, is difficult to measure and numerous organ-izations are struggl

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

Recently, information systems(IS) outsourcing has become a very important management strategy of implementing IS and many studies on the IS outsourcing approach had been largely performed in the organizations, but it isn't still show how to outsource the IS functions and how to decide quantitative magnitude for judgement. To offer a quantitative decision model that can help practitioners set priority and reap the most benefits from outsourcing, we show outsourcing structure including 3 factors(strategic benefit, economic benefit and technological benefit) and sub-levels which. is different from the Yang and Huang's model. Also, we compute the weight of alternatives using analytic hierarchy process to find a priority of the IS outsourcing. As a result of analysis, we suggest systematic steps and quantitative model to increase the precision of decision making.

Utilization rate for the gate and required time and walking distance to boarding flight are important measures for the gate management and passenger's convenience estimation. So, the main purpose of the gate management are the maximization of utilization rate and improvement of airport terminal user's convenience through the efficient gate management. This study intends to maximize the utilization rate of usable gates by considering layout, terminal configuration, local passenger of the airport and development of gate assignment algorithm and DSS which maximizes the gate utilization and minimizes the passengers' walking distance. And the decision support system can provide an efficient means of airport management of airport using an assignment algorithm.

Utilization rate for the restrictive gate and required time and walking distance to board are to be a measure for the gate management and passenger's convenience estimation. So, the main purpose of the gate management are maximization of utilization rate and increment of airport terminal user's convenience through the efficient gate management. This study intends to maximize the utilization rate on usable gates by concerning about layout, terminal configuration, local passenger of the airport and development of gate assignment algorithm and DSS which maximize the gate utilization and minimize the passenger's walking distance.