【摘要】：粗糙集理論是波蘭數(shù)學(xué)家Pawlak在1982年提出的，是概率論、模糊集理論之后又一處理模糊性、不確定性數(shù)據(jù)的數(shù)學(xué)工具。該理論的特點(diǎn)是不需要任何先驗(yàn)知識(shí)或附加信息，在多屬性決策問題中的指標(biāo)篩選和排序選優(yōu)等方面有很好的應(yīng)用前景。 經(jīng)典粗糙集主要針對(duì)完備信息系統(tǒng)，然而在現(xiàn)實(shí)生活中由于數(shù)據(jù)測(cè)量的誤差，對(duì)數(shù)據(jù)的理解或獲取的限制等原因，使得在知識(shí)獲取時(shí)往往面臨著不完備的信息系統(tǒng)，即可能存在部分對(duì)象的一些屬性值未知的情況，本文首先在完備信息下模糊決策可變精度粗糙集模型得基礎(chǔ)上，根據(jù)隸屬度函數(shù)給出了處理不完備信息多屬性決策下的粗糙集屬性約簡(jiǎn)算法，并通過算例分析檢驗(yàn)?zāi)Ｐ偷目尚行浴?另外，，針對(duì)多屬性決策問題中的排序選優(yōu)問題，以往研究大多默認(rèn)屬性之間是可以相互補(bǔ)償?shù)�，而現(xiàn)實(shí)生活中也存在著屬性之間不能完全補(bǔ)償?shù)那闆r。針對(duì)這一情況，本文提出了粗糙集層次權(quán)重確定法和相對(duì)信息熵改進(jìn)的信息熵?cái)U(kuò)展粗糙集排序模型，綜合考慮方案的整體性和均衡性，能夠在一定程度上提高排序結(jié)果的準(zhǔn)確性。 最后將排序模型應(yīng)用于供應(yīng)商選擇，構(gòu)建了適合化工設(shè)備零部件供應(yīng)商選擇的指標(biāo)體系，并采集數(shù)據(jù)通過實(shí)例分析說明其應(yīng)用價(jià)值，比較線性加權(quán)模型和改進(jìn)后模型的評(píng)價(jià)值，計(jì)算結(jié)果表明改進(jìn)后的模型更加符合企業(yè)的實(shí)際選擇結(jié)果，證明方法是科學(xué)有效的。
[Abstract]:Rough set theory, proposed by Polish mathematician Pawlak in 1982, is a mathematical tool for dealing with fuzzy and uncertain data after probability theory and fuzzy set theory. The characteristic of this theory is that it does not need any prior knowledge or additional information, and it has a good application prospect in the field of index selection and ranking selection in multi-attribute decision making problems. The classical rough set is mainly aimed at the complete information system. However, in real life, because of the error of data measurement, the limitation of data understanding or acquisition, etc., it is often faced with incomplete information system when acquiring knowledge. That is to say, there may be some unknown attribute values of some objects. Firstly, based on the fuzzy decision variable precision rough set model under complete information, According to the membership function, a rough set attribute reduction algorithm for multi-attribute decision making with incomplete information is presented, and the feasibility of the model is verified by an example. In addition, in order to solve the problem of sorting and optimization in multi-attribute decision making, most of the previous researches have shown that the default attributes can compensate each other, but in real life, there are some cases in which the attributes can not be fully compensated. In this paper, a hierarchical weight determination method based on rough set and an improved information entropy extended rough set sorting model are proposed in this paper, considering the integrity and equilibrium of the scheme. It can improve the accuracy of sorting results to a certain extent. Finally, the ranking model is applied to supplier selection, and the index system suitable for the supplier selection of chemical equipment parts is constructed. The application value of the model is illustrated by a practical example, and the evaluation values of the linear weighted model and the improved model are compared. The calculation results show that the improved model is more in line with the actual selection results of the enterprise, and proves that the method is scientific and effective.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F274;F224

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