• Journal of applied mathematics & informatics
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• v.42 no.3
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• pp.521-538
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• 2024

In this paper, using the notion of the imprecise set, the idea of an imprecise group is introduced including some examples. The two key rules of classical set theory are obeyed by this extended version of fuzzy sets, which the existing complement definition of a fuzzy set failed to do. With the support from general group theory, the paper also provides some fundamental properties of an imprecise group here. Additionally, it includes a few characteristics of imprecise subgroups, and abelian imprecise group.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.321-331
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• 1997

Measurement of attributes is often highly subjective and imprecise, yet most MADM methods lack provisions for handling imprecise data. Frequently, decision makers must establish a ranking within a finite set of alternatives with respect to multiple attributes which have varying degrees of importance. The problem is more complex if the evaluations of alternatives according to each attribute are not expressed in precise numbers, but rather in fuzzy numbers. Analysis must allow for lack of precision and partial truth. The advantages of a fuzzy approach for MADM are that a decision maker can obtain efficient solutions all at once without trial and error, and that this approach provides better support for judging the interactive improvement of solutions in comparison with o decision making method. The algorithm used in this study is based on the concepts of vague set theory. Linguistic variables and vague values are used to facilitate a decision maker's subjective assessment about attribute weightings and the appropriateness of alternative versus selection attributes in order to obtain final scores which are called vague appropriateness indices. A numerical example is presented to show the practical applicability of this approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.117-131
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• 2015

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.71-76
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• 2011

The problem of decision making under imprecise environments are widely spread in real life decision situations. We present a method of object recognition from imprecise multi observer data, which extends the work of Roy and Maji [J Compu. Appl. Math. 203(2007) 412-418] to generalized intuitionistic fuzzy soft set theory. The method involves the construction of a comparison table from a generalized intuitionistic fuzzy soft set in a parametric sense for decision making.

• Journal of applied mathematics & informatics
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• v.35 no.3_4
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• pp.331-347
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• 2017

Soft sets are fantastic mathematical tools to handle imprecise and uncertain information in complicated situations. In this paper, we defined the hybrid structure which is the combination of soft set and complex number representation of intuitionistic fuzzy set. We defined basic set theoretic operations such as complement, union, intersection, restricted union, restricted intersection etc. for this hybrid structure. Moreover, we developed this theory to establish some more set theoretic operations like Disjunctive sum, difference, product, conjugate etc.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.923-932
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• 2001

In this paper, An imprecise real-time task sets composed of multimedia datas are generated using several parameters and an algorithm which can analyse schedulability of generated imprecise real-time task sets before execution of this task sets is proposed. Also, The schedulability of task set depends on variation of parameter values which were used during the generation of the task set is studied. As a result of experiment, It isproved that the schedulability of task set is more and more weak as large as execution requirement time of mandatory subtasks and, as many as the number of tasks which can be scheduled at some instant. The schedulabilty analysis algorithm which is presented on this paper is expected to use effectively on QoS service of multimedia datas.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.468-473
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• 2011

The goal of a project manager is generally to minimize the cost of the project and also to cope with uncertainty. This paper deals with the problem of project scheduling a set of activities satisfying precedence constraints in order to minimize the sum of the costs associated with the starting times of the activities in the network with imprecise activity durations, represented by means of interval or fuzzy numbers. So far this problem has been completely solved by several authors when the activities durations have crisp values. However, they do not consider the imprecision in activity durations in their models. Here the framework of possibility theory is proposed to solve this problem. In fuzzy arithmetic, usually, the interval calculations are used for the aim of complexity reduction and simplification. Thus the case of interval-valued durations is first addressed, and then extended to fuzzy intervals. A numerical example is used to illustrate the developed concept.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.81-86
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• 1997

It is proposed, for fuzzy combustion control system of refuse incinerator to find the relationship between inputs and outputs and to generate rules to control by using rough set theory. It is not easy to find out the corresponding inputs for each output and the control rules with incomplete or imprecise information consisting expert knowledge, process and manipulator values in the field, and operation manual for the given system. Most decision problems can be formulated employing decision table formalism. A decision table on fuzzy combustion control system for refuse incinerator is simplified and produces control(rules). The I/O realtions and the control rules found by rough set theory are compared with the previous result.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.197-200
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• 1991

In this paper, a technique to reduce the computational effort of Hough Transform used to recognize digital circles is proposed. The technique, which is a version of the Hough Transform uses bisectors of circles cords to estimate the center of circles. This system which is implemented in this paper gets center, radius, thickness of circular fiducials regardless of input form. This system uses fuzzy set theory which deals with 'ambigousness', In order to recognize imprecise circular fiducial.

• Nuclear Engineering and Technology
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• v.23 no.3
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• pp.285-298
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• 1991

An example application of the fuzzy set theory is first made to a simple portion of a given accident progression event tree with typical qualitative fuzzy input data, and thereby computational algorithms suitable for application of the fuzzy set theory to the accident progression event tree analysis are identified and illustrated with example applications. Then the procedure used in the simple example is extended to extremely complex accident progression event trees with a number of phenomenological uncertainty issues, i.e., a typical plant damage state‘SEC’of the Zion Nuclear Power Plant risk assessment. The results show that the fuzzy averages of the fuzzy outcomes are very close to the mean values obtained by current methods. The main purpose of this paper is to provide a formal procedure for application of the fuzzy set theory to accident progression event trees with imprecise and qualitative branch probabilities and/or with a number of phenomenological uncertainty issues.