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APPLICATION OF FUZZY ENVELOPMENT DATA ANALYSIS IN PROJECT MANAGEMENT OF HIGH SPEED TRAIN (Beijing --- Guangzhou // CHINA)

APPLICATION OF FUZZY ENVELOPMENT DATA ANALYSIS IN PROJECT MANAGEMENT OF HIGH SPEED TRAIN
(Beijing --- Guangzhou // CHINA)

مقاله نهم من با عنوان :

APPLICATION OF FUZZY ENVELOPMENT DATA ANALYSIS IN PROJECT MANAGEMENT OF HIGH SPEED TRAIN
(Beijing --- Guangzhou // CHINA)

در کنفرانس بین المللی :

13TH IRANIAN CONFERENCE ON FUZZY SYSTEMS

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ABSTRACT

The concept of fuzzy sets is presented as a new tool for the formulation and solution of systems and decision problems which contain fuzzy components or fuzzy relationships. Fuzzy logic is widely used in machine control and nowadays for project management. The term itself inspires a certain skepticism, sounding equivalent to half-baked logic or bogus logic, but the "fuzzy" part does not refer to a lack of rigor in the method, rather to the fact that the logic involved can deal with concepts that cannot be expressed as "true" or "false" but rather as "partially true".
Although genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller.
This makes it easier to mechanize tasks that are already successfully performed by humans. considerable amount of ‘engineering feel’ for the process which is difficult to quantify and utilize. Evaluating the performance of activities or organizations by traditional data envelopment analysis (DEA) models requires crisp input/output data. However, in real-world problems inputs and outputs are often imprecise. This paper develops DEA models using imprecise data represented by fuzzy sets. It is shown that fuzzy DEA models take the form of fuzzy linear programming which typically are solved with the aid of some methods to rank fuzzy sets.
Many complex industrial processes cannot be satisfactorily controlled using the results of modern control theory, mainly because their precise structure is unknown. However, this is often balanced by a As an alternative, a possibility approach is introduced in which constraints are treated as fuzzy events. The approach transforms fuzzy DEA models into possibility DEA models by using possibility measures of fuzzy events .
We show that for the special case, in which fuzzy membership functions of fuzzy data are of trapezoidal types, possibility DEA models become linear programming models. A numerical experiment is used to illustrate the approach and compare the results with those obtained with alternative approaches.as we know the new theories of fuzzy system and DEA use and apply in project management.
Data envelopment analysis (DEA) as introduced by Charnes, Cooper, and Rhodes (1978) is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input–output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors.
Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on α-cut. One drawback of the α-cut approach is that it cannot include all information about uncertainty.
As we know Project management is a carefully planned and organized effort to accomplish a successful project. Project management includes developing a project plan, which includes defining and confirming the project goals and objectives, identifying tasks and how goals will be achieved, quantifying the resources needed, and determining budgets and timelines for completion. It also includes managing the implementation of the project plan, along with operating regular 'controls' to ensure that there is accurate and objective information on 'performance' relative to the plan, and the mechanisms to implement recovery actions where necessary. nowadays some of the projects are too big and making a decision and control of all part of the project is going to be very complex therefore applying the new theory like fuzzy system on it is very require.
In this project on the bases of investigation on high speed train project in china, rout Beijing to Guangzhou, the biggest line in the world and big national project in CHINA , we tried to explain how we could use fuzzy system in project management of this project in several phases and explain our result in the terms of fuzzy envelopment data analysis.

key words : Data envelopment analysis; Fuzzy mathematical programming; Possibility theory; Efficiency analysis, Project Management, Interval data Decision making unit
REFERENCES
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[2] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification and SceneAnalysis. New York: Wiley, 1999.

[3] J. C. Bezdek, Pattern Recognition With Fuzzy Objective FunctionAlgorithm. New York: Plenum, 1981.

[4] R. Krishnapuram and J. M. Keller, “A possibilistic approach to clustering,” IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98–110, May 1993.

[5] N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, “A possibilistic fuzzyc-means clustering algorithm,” IEEE Trans. Fuzzy Syst., vol. 13, no. 4,pp. 517–530, Aug. 2005.

[6] F. Masulli and S. Rovetta, “Soft transition from probabilistic to possibilisticfuzzy clustering,” IEEE Trans. Fuzzy Syst., vol. 14, no. 4, pp. 516–527,Aug. 2006.

[7] Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning About Data.Dordrecht, The Netherlands: Kluwer, 1991.

[8] P. Lingras and C. West, “Interval set clustering of web users with roughK-means,” J. Intell. Inf. Syst., vol. 23, no. 1, pp. 5–16, 2004.

[9] D. Dubois and H. Prade, “Rough fuzzy sets and fuzzy rough sets,” Int. J.Gen. Syst., vol. 17, no. 2/3, pp. 191–209, 1990.

[10] S. Mitra, H. Banka, and W. Pedrycz, “Rough–fuzzy collaborativeclustering,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 4,pp. 795–805, Aug. 2006.

[11] P. Maji and S. K. Pal, “Rough–fuzzy C-medoids algorithm and selection of bio-basis for amino acid sequence analysis,” IEEE Trans. Knowl. DataEng., vol. 19, no. 6, pp. 859–872, Jun. 2007.

[12] G. James, Modern Engineering Mathematics. Reading, MA: Addison-
Wesley, 1996.

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