• Korean Management Science Review
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• v.11 no.2
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• pp.185-206
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• 1994

This paper investigated, using a flexible approach, the effects of the psychological type of the decision maker and the advanced format of information presentation on decision maker performance in a computer-simulated production game. The current sutdy was guided by a model derived from a general model developed by Chervany, Dickson, and Kozar(1972). The experimental model had two dependent variables; total profit and decision making time. Three independent variables representing the psychological type of the decision maker, the report format, and decision aids were used in this study.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.207-210
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• 2001

This Paper presents a design of the fuzzy decision maker analyzable of output result of ECG signals. The fuzzy decision maker proposed are divided into two groups whose functions are different each other. The one rules when decision of heart rates, The other decision values for an interval of each points of waveform using of which static state values and abnormal values. We have chosen several variable used for composing condition and action part by knowledge of an Expert The result of outputs with fuzzy rules suggested was a proved of satisfied with by classify ECG arrythmia signals

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.22
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• pp.51-57
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• 1990

A new interactive multiobjective decision making technique, which is called the fuzzy sequential proxy optimization technique, has been proposed. This technique is the revised version the sequential proxy optimization technique that the decision-maker's marginal rates of substitution is interpreted as type of L-R fuzzy numbers. It used to the square of normalized scalar product as the doptimalilry condition. However, this technique ignores the imprecise nature of a decision-maker's judgement of marginal rates of substitution. Also, it have a shortcoming that can be only applied over three objective functions. In this paper, considering the imprecise nature of a decision-maker's judgement, we presents an interactive fuzzy decision-making method on the basis of the decision-maker's MRS presented through the use of five types of membership functions including non-linear functions. FORTRAN programs that run in conversational mode are developed to implement man-machine interactive procedure.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.61-76
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• 1995

The "optimal" solution for a decision making problem should be the one that best reflects the decision-maker's preference. For MADM (Multi-Attribute Decision-Making) problems, however, finding an optimal solution is difficult, especially when the number of alternatives, or that of attributes is relatively large. Most of the existing mathematical approaches arrive at a final solution on the basis of many unrealistic assumptions, without reflecting the decision-maker's preference structure exactly. To remedy this, some interactive methods have been proposed, but most of them require a large amount of information growing exponentially as the number of alternatives, or that of attributes increases. Therefore it is difficult for the decision-maker to maintain consistency throughout the decision making process. In this paper, an interactive method which finds optimal solutions for deterministic MADM problems with many attributes and alternatives is proposed. Instead of considering all the attributes simultaneously, this method partitions all the attributes into several mutually independent subgroups and considers one of them at each of preordered steps, where the alternatives are eliminated until the optimal one is obtained. The efficiency of the method lies in the fact that the amount of neccessary information is reduced significantly, and even further if a suboptimal solution is acceptable to the decision-maker.ion-maker.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.33-44
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• 1989

This paper presents stochastic-statistical dominance rules which eliminate dominated alternatives thereby reduce the number of satisficing alternatives to a manageable size so that the decision maker can choose the best alternative among them when neither the utility function nor the probability distribution of outcomes is exactly known. Specifically, it is assumed that only the characteristics of the utility function and the value function are known. Also, it is assumed that prior probabilities of the mutually exclusive states of nature are not known, but their relative bounds are known. First, the notion of relative risk aversion is used to describe the decision maker's attitude toward risk, which is defined with the acknowledgement that the utility function of the decision maker is a composite function of a cardinal value function and a utility function with-respect to the value function. Then, stochastic-statistical dominance rules are developed to screen out dominated alternatives according to the decision maker's attitude toward risk represented in the form of the measure of relative risk aversion.

• IE interfaces
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• v.6 no.2
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• pp.151-169
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• 1993

In this research, we proposed "coefficient of inequality" as a measure of distributional inequality for an alternative, which is defined as the area between the diagonal line from 0 to 1 and the Lorenz curve of the given alternative. Next, we showed theoretical relationship between stochastic dominance and the coefficient of inequality as a means to determine the preferred alternative when decision is made with incomplete information about decision maker's utility function. Then, two experiments were performed to test subject‘s attitude toward risk. The results of the experiments support the idea that when a decision maker is risk averse or risk prone, he/she can use the coefficient of inequality as a decision rule to choose the preferred alternative instead of using stochastic dominance. Thus, according to decision maker’s attitude toward risk, the decision rule proposed here can be used as a valuable aid in decision making under uncertainty with incomplete information.

• Journal of the Korean Solar Energy Society
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• v.24 no.4
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• pp.55-63
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• 2004

The purpose of this study is to development of life cycle cost analysis methodology of HVAC system for decision maker. The results of this study are as follows; maintenance/management, equipment construction, planning/design, and demolition/sell phases (1) To develop the cost breakdown structure for LCC in HVAC system, this study apply the method of additional pertinent level, title, CBS number, block number and variable index. (2) LCC analysis order of HVAC system compose four phase. (3) Life cycle costing influence diagram can bring us to make the most efficient decision through a visual graphical diagram that is shown relationship among variables and that decision maker traces easily from life cycle cost analysis situation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.380-383
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• 2003

In this paper, we propose a fuzzy group decision making method for multiple decision maker-multiple objective programming problems to obtain the agreeable solution. In the proposed method, considering the vague nature of human subjective judgement it is assumed that each of multiple decision makers has a fuzzy goal for each of his/her own objective functions. After eliciting the membership functions from the decision makers for their fuzzy goals, total M-Pareto optimal solution concept is defined in membership spaces in order to deal with multiple decision maker-multiple objective programming problems. For generating a candidate of the agreeable solution which is total M-Pareto optimal, the extended weighted minimax problem is formulated and solved for some weighting vector which is specified by the decision makers in their subjective manner, Given the total M-Pareto optimal solution, each of the derision makers must either be satisfied with the current values of the membership functions, or update his/her weighting vector, However, in general, it seems to be very difficult to find the agreeable solution with which all of the decision makers are satisfied perfectly because of the conflicts between their membership functions. In the proposed method, each of the decision makers is requested to estimate the degree of satisfaction for the candidate of the agreeable solution. Using the estimated values or satisfaction of each of the decision makers, the core concept is desnfied, which is a set of undominated candidates. The interactive algorithm is developed to obtain the agreeable solution which satisfies core conditions.

• Journal of the Korean Operations Research and Management Science Society
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• v.8 no.1
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• pp.31-40
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• 1983

An uncertain dynamic evolving process has been a continuing challenge to decision problems. The dynamic random variable (drv) changes which characterize such a process are very important for the decision-maker in selecting a course of action in a world that is perceived as uncertain, complex, and dynamic. Using this subjective point prediction algorithm based on a modified recursive filter, the decision-maker becomes to have periodically changing plausible points with the passage of time.