Study Of The DEA-DA Fuzzy Model With Interval Numbers And Its Applications On The Performance Measurement For Agricultural Enterprises

This paper comes from the scientific and technological development projects in Jilin Province (item No.: 20060529) "DEA-DA fuzzy pattern recognition algorithm and its application" and primarily bases on data envelopment analysis (DEA) and discriminant analysis (DA) called DEA-DA discrimination model established by Sueyoshi. DEA-DA discriminant model achieves advantage of high discriminant efficiency and convenient algorithms. But it is established on the fact that the model can only deal with the sample unit with certainty data. While in true life, whether in the field of engineering, the economic system and other aspects, fuzzy uncertainty exist widely which restrict the using of the model. In order to improve the application of the model, the study has arranged interval number into the DEA-DA discriminant model, we call the new model "DEA -DA discriminant model with interval number". The application on the performance measurement for agricultural enterprises will not only verify the effectiveness of the method, but also provide a scientific advice from the information digged from the outcome for decision-making. At last, the paper has added all of the reference documents.The full text is divided into five chapters as follows:Chapter 1: Introduction: This paper expounded the background to the study, meaning, as well as the main content, technology routes and methods.Chapter 2: Study of the DEA-DA model with interval numbers and its sensitivity analysis: This chapter introduced the theory of DEA-DA discriminant model. Then, after a detailed studing of several samples' discrimination laws with interval numbers, we integrated interval numbers into the Extended DEA-DA model called Extended DEA-DA model with interval number. Furthermore, we studied the sensitivity analysis of the integration model through analyzing the feasible solution of linear programming in order to discuss the disturbance of the upper and lower vertex of interval number.Then we validate the efficiency of the model and the disturbance throught illustrations .Chapter 3: interval containing the number of Two-stage MIP DEA-DA model: As Two-stage MIP DEA-DA model is superior whether in accuracy in identifying or in model algorithm than Extended DEA-DA model, this chapter structure Two-stage MIP DEA-DA model with interval number, so as to choose a more aprropriate of the two models for use. In addition, this chapter also has re-established a new Two-stage discrimination model which has the function that can discriminate more than two samples on the basis of the model of the Sueyoshi.At the same time a example has tested the effectiveness of discriminant function.Chapter 4: Study of the DEA-DA fuzzy model with interval numbers and their Applications on the Performance Measurement for Agricultural Enterprises: This chapter has introduced universal methods of performance measument and the characteristic of performance measurement for agricultural enterprises.Based on the literature [54] which argued about index system for the performents of agricultural enterprise, we have reaserched the performence listed 27 agriculture-related enterprises using Extended DEA-DA model with interval numbers. After that we analysed the performance of two of these 27 enterprises.Chapter 5: Conclusions and expectations of the paper.draws the main creating points of the paper and present the main direction and the field which should be studied in the future.