• Management Science and Financial Engineering
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• v.14 no.2
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• pp.67-87
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• 2008

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations which is of vital practical importance in managerial decision making. While DEA assumes exact input and output data, the development of imprecise DEA (IDEA) broadens the scope of applications to efficiency evaluations involving imprecise information which implies various forms of ordinal and bounded data possibly or often occurring in practice. The primary purpose of this article is to characterize the variable efficiency in IDEA. Since DEA describes a pair of primal and dual models, also called envelopment and multiplier models, we can basically consider two IDEA models: One incorporates imprecise data into envelopment model and the other includes the same imprecise data in multiplier model. The issues of rising importance are thus the relationships between the two models and how to solve them. The groundwork we will make includes a duality study which makes it possible to characterize the efficiency solutions from the two models. This also relates to why we take into account the variable efficiency and its bounds in IDEA that some of the published IDEA studies have made. We also present computational aspects of the efficiency bounds and how to interpret the efficiency solutions.

• 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.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.9
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• pp.22-32
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• 1997

This paper proposes an imprecise compuitation model for DCT considering QOS of images and a two dimensional DCT architecture for imprecise computations. In case that many processes are scheduling in a hard real time system, the system resources are shared among them. Thus all processes can not be allocated enough system resources (such as processing power and communication bandwidth). The imprecise computtion model can be used to provide scheduling flexibility and various QOS(quality of service)levels, to enhance fault tolerance, and to ensure service continuity in rela time systems. The DCT(discrete cosine transform) is known as one of popular image data compression techniques and adopted in JPEG and MPEG algorithms since the DCT can remove the spatial redundancy of 2-D image data efficiently. Even though many commercial data compression VLSI chips include the DCST hardware, the DCT computation is still a very time-consuming process and a lot of hardware resources are required for the DCT implementation. In this paper the DCT procedure is re-analyzed to fit to imprecise computation model. The test image is simulated on teh base of this model, and the computation time and the quality of restored image are studied. The row-column algorithm is used ot fit the proposed imprecise computation DCT which supports pipeline operatiions by pixel unit, various QOS levels and low speed stroage devices. The architecture has reduced I/O bandwidth which could make its implementation feasible in VLSI. The architecture is proved using a VHDL simulator in architecture level.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.7
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• pp.1304-1320
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• 1994

There are two computation techniques in real time systems : precise and imprecise computation. The imprecise computation technique is a means to provide scheduling flexibility in real time systems. The studies on imprecise scheduling using queueing theoretical formulation up to data are to explicitly quantify the costs and benifits in trade-off between the average result quality and the average waiting time of tasks. This paper uses two imprecise scheduling schemes and solves the imprecision probability, the probability of any task being imprecise under two imprecise scheduling schemes and analyzes the dependence of the imprecision probability on several parameters os the monotone imprecise system.

• IE interfaces
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• v.19 no.4
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• pp.291-299
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• 2006

In order to enhance sustainable war fighting capabilities, it is important to maintain a good ammunition support system. In this paper, we evaluate the performance of ammunition companies using Imprecise Data Envelopment Analysis (IDEA)-BCC and IDEA-Additive model, which can deal with imprecise data in DEA. The input variables of IDEA models were selected by stepwise multiple regression analysis. With the regression model, we could choose the number of soldiers, officers, and ammunition warehouses as input variables that have significant effects on the output performance. Then, we applied the IDEA-BCC model with the concept of potential efficiency. The results of the model indicate that 8 out of 16 ammunition companies are efficient, 7 are inefficient, and 1 is potentially efficient. We could also identify the possible input excesses and output shortfalls to reach the efficient frontier using the IDEA-Additive model.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.433-443
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• 2006

In [1], they build two populations' cellular automata model with predation based on the Penna model. In this paper, uncertain aspects and problems of imprecise and vague data are considered in this model. A fuzzy cellular automata model containing movable wolves and sheep has been built. The results show that the fuzzy cellular automata can simulate the classical CA model and can deal with imprecise and vague data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.171-177
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• 2011

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1707-1714
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• 2006

In order to enhance sustainable war fighting capabilities, it is important to maintain a good ammunition support system. In this paper, we evaluate the performance of Ammunition companies using Imprecise Data Envelopment Analysis (IDEA)-BCC and IDEA-Additive model, which can deal with imprecise data in DEA. In order to select a list of input and output variables, we used a multiple regression analysis. We could choose input variables that have significant effects on the output performance with stepwise regression model. From the regression analysis, the number of soldiers, officers, and ammunition warehouses were selected as the input variables. Seven out of sixteen Ammunition companies were found to be inefficient by the IDEA-BCC model. And using IDEA-Additive model, we could identify the input excess and the output shortfall in reaching at a point on the efficiency frontier.

• 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 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.