• Journal of Mechanical Science and Technology
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• v.20 no.6
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• pp.731-737
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• 2006

Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer.

• Korean Journal of Construction Engineering and Management
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• v.5 no.5 s.21
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• pp.143-150
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• 2004

Nowaday, The tendency of complexity and extension of construction fields increase the need for efficient works managements like a construction management. Consequently, by the introduction of Decision-Making Theories, researches for improving construction field's efficiencies are actively performed. Fuzzy Analytical Hierarchy Process method is invented, so that describes a decision maker's ambiguous linguistic judgment with fuzzy numbers. but most of researches on Fuzzy-AHP use symmetric triangular fuzzy function for estimating each evaluation item with the consequence that exact judgments are impossible. those limits are caused by the point that employed fuzzy ranking methods can not support dissymmetric fuzzy numbers. In this research, we aims to overcome this problem with R.P.R(Relative Preference Ratio) method and suggest improved Fuzzy-AHP method which can use dissymmetric fuzzy triangular numbers.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.69-74
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• 2004

A sequencing problem is to find an ordered sequence of some entities which maximizes (or minimize) the domain specific objective function. As some typical examples of sequencing problems, there are traveling salesman problem, job shop scheduling, flow shop scheduling, and so on. This paper introduces a new type of sequencing problems, named a sequencing problem with fuzzy preference relation, where a fuzzy preference relation is provided for the evaluation of the quality of sequences. It presents how such a problem can be formulated in terms of objective function. It also proposes a genetic algorithm applicable to such a sequencing problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.94-95
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• 2006

A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. In this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.326-331
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• 2006

A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. In this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.414-425
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• 2010

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.181-187
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• 2014

Dealing with uncertainty is always a challenging problem. Intuitionistic fuzzy sets was presented to manage situations in which experts have some membership and non-membership value to assess an alternative. Hesitant fuzzy sets was used to handle such situations in which experts hesitate between several possible membership values to assess an alternative. In this paper, the concept of intuitionistic hesitant fuzzy set is introduced to provide computational basis to manage the situations in which experts assess an alternative in possible membership values and non-membership values. Distance measure is defined between any two intuitionistic hesitant fuzzy elements. Fuzzy technique for order preference by similarity to ideal solution is developed for intuitionistic hesitant fuzzy set to solve multi-criteria decision making problem in group decision environment. An example is given to illustrate this technique.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.716-720
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• 2006

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user's, preference on fuzzy values and select one which fits to his preference. Users' preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users' answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users' preference than other methods.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.461-468
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• 2017

The current available recommendation systems for mobile games have a couple of problems. First, there is no knowing whether they make a pattern recommendation for games that actual users prefer or for games that they are simply interested in. It is also impossible to know the subjective preference of users in a direct manner. An AHP(Analytic Hierarchy Process)-based recommendation system for mobile games was thus developed to reflect the subjective preference of users directly, but it had its own problem since the degree of preference could vary among users in spite of the same scale for their preferable items. In an effort to solve those problems, this study implemented a recommendation system for mobile games by applying triangular fuzzy numbers of the Fuzzy-AHP technique and the independence of evaluation items in the Bayesian Network. The findings show that the proposed recommendation system recorded the highest accuracy of recommendation results and the highest level of user satisfaction.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.640-645
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• 1998

A Sequencing problem is one to find an ordered sequence of some entities which maximizes (or minimize) some objective function. This paper introduces an new type of sequencing problems, named a Sequencing problem with fuzzy preference relation is previded for the evaluation of the quality of sequences, It presents how such a problem can be formulated in the point of objective function. In addition, it proposes a genetic algorithm applicable to such a sequencing problem.