誘導型語言算子的多屬性群決策方法研究
發(fā)布時間：2018-08-04 14:43
【摘要】：語言型多屬性群決策在社會、市場分析、經(jīng)濟等方面應用廣泛,它是決策科學理論的重要組成部分。由于在決策過程中決策環(huán)境的不確定性,在面對復雜問題進行決策時,往往不能利用定量的方法進行決策分析,因此,專家利用語言信息進行方案屬性的評價往往更加直觀和方便,減少了信息的丟失,同時,利用群決策可以避免因其中某個專家決策的失誤而導致決策結(jié)果評判錯誤,避免不良后果的情況發(fā)生,保證了決策結(jié)果的準確性,特別是對于電力建設項目而言,決策的結(jié)果直接影響到項目實施能否達到預期。誘導型語言算子常用于多屬性決策問題,其主要特征是利用誘導變量進行指標值的重新排列,根據(jù)排列后的位置進行加權(quán)集結(jié)。本文在誘導型語言算子的基礎上進行多屬性群決策問題研究,主要研究內(nèi)容包括:(1)通過研究誘導有序加權(quán)平均(IOWA)算子和概率有序加權(quán)平均(POWA)算子的性質(zhì),了解誘導變量和概率變量形成的集成算子在多屬性群決策中的應用,在此基礎上研究誘導語言概率有序加權(quán)平均(ILPOWA)算子,分析不同重要程度下集成算子的集結(jié)結(jié)果。(2)將誘導語言概率有序加權(quán)平均(ILPOWA)算子推廣到誘導不確定語言廣義概率有序加權(quán)平均(IULGPOWA)算子,其中,對于指標值以不確定語言變量所表示的情形,利用誘導不確定語言廣義有序加權(quán)平均(IULGOWA)算子和不確定語言廣義概率加權(quán)平均(ULGPWA)算子形成的IULGPOWA算子進行集結(jié),這個新的集成算子不僅考慮了參數(shù)所在位置重要性程度,還考慮了參數(shù)的概率,與其他集成算子更具一般性,同時,根據(jù)電力建設項目特點,對新的集成算子進行實證分析。(3)基于誘導二元語義廣義有序加權(quán)平均(2TLGOWA)算子和二元語義廣義概率加權(quán)平均(2TLGPA)算子,形成誘導二元語義廣義概率有序加權(quán)平均(2TLIGPOWA)算子,利用二元語義描述指標值,有效地減少信息的丟失,同時,在廣義的環(huán)境下考慮概率信息和決策者的態(tài)度特征,在最大值和最小值中提供了一個集結(jié)算子族,闡述了該決策模型的廣義性,與其他集成算子更具一般性,并在電力建設項目背景下進行廣義集成算子的多屬性群決策實證分析,驗證了其有效性。通過構(gòu)建了兩類集成算子,能夠?qū)γ枋稣T導語言環(huán)境下大部分的多屬性決策問題,同時所提供的算例有效的證明了其在實際中的可行性和有效性。
[Abstract]:Linguistic multi-attribute group decision making is widely used in society, market analysis, economy and so on. It is an important part of decision science theory. Because of the uncertainty of the decision-making environment in the decision-making process, the quantitative method can not be used to make decision analysis in the face of complex problems. It is more intuitive and convenient for experts to evaluate scheme attributes by using language information, which reduces the loss of information. At the same time, the use of group decision making can avoid the error of decision result because of the error of one expert decision. In order to avoid the adverse consequences and ensure the accuracy of the decision results, especially for the electric power construction projects, the decision results directly affect the implementation of the project to achieve the expected. Inductive language operators are often used in multi-attribute decision making problems. The main feature of the operators is that the index values are rearranged with induced variables and weighted aggregation is carried out according to the arranged positions. On the basis of inductive language operators, this paper studies the problem of multi-attribute group decision making. The main contents are as follows: (1) the properties of induced ordered weighted average (IOWA) operator and probabilistic ordered weighted average (POWA) operator are studied. The application of the integration operator formed by induced variables and probabilistic variables in multi-attribute group decision making is studied. On this basis, the probabilistic ordered weighted average (ILPOWA) operator of inductive language is studied. The aggregation results of integration operators with different degrees of importance are analyzed. (2) the generalized probabilistic ordered weighted average (ILPOWA) operator of induced language is extended to the generalized probabilistic ordered weighted average (IULGPOWA) operator of induced uncertain language. For the case where the index value is expressed as an uncertain language variable, the IULGPOWA operators formed by the generalized ordered weighted average (IULGOWA) operator of induced uncertain language and the generalized probabilistic weighted average (ULGPWA) operator of uncertain language are used to aggregate. This new integration operator takes into account not only the importance of the location of the parameters, but also the probability of the parameters, which is more general than other integration operators. At the same time, according to the characteristics of the electric power construction project, (3) based on the generalized ordered weighted average (2TLGOWA) operator and the generalized probability weighted average (2TLGPA) operator, the generalized ordered weighted average (2TLIGPOWA) operator is formed. The binary semantics is used to describe the index value, which effectively reduces the loss of information. At the same time, considering the probability information and the attitude characteristics of the decision maker in the generalized environment, a family of aggregation operators is provided in the maximum and minimum values. The generality of the decision model is expounded, which is more general than other integration operators, and an empirical analysis of multi-attribute group decision making of generalized integration operator under the background of electric power construction project is carried out, which verifies its validity. By constructing two kinds of ensemble operators, most of the multi-attribute decision making problems in the environment of description induction language can be solved. At the same time, the examples are provided to prove its feasibility and validity in practice.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O225
本文編號：2164200
[Abstract]:Linguistic multi-attribute group decision making is widely used in society, market analysis, economy and so on. It is an important part of decision science theory. Because of the uncertainty of the decision-making environment in the decision-making process, the quantitative method can not be used to make decision analysis in the face of complex problems. It is more intuitive and convenient for experts to evaluate scheme attributes by using language information, which reduces the loss of information. At the same time, the use of group decision making can avoid the error of decision result because of the error of one expert decision. In order to avoid the adverse consequences and ensure the accuracy of the decision results, especially for the electric power construction projects, the decision results directly affect the implementation of the project to achieve the expected. Inductive language operators are often used in multi-attribute decision making problems. The main feature of the operators is that the index values are rearranged with induced variables and weighted aggregation is carried out according to the arranged positions. On the basis of inductive language operators, this paper studies the problem of multi-attribute group decision making. The main contents are as follows: (1) the properties of induced ordered weighted average (IOWA) operator and probabilistic ordered weighted average (POWA) operator are studied. The application of the integration operator formed by induced variables and probabilistic variables in multi-attribute group decision making is studied. On this basis, the probabilistic ordered weighted average (ILPOWA) operator of inductive language is studied. The aggregation results of integration operators with different degrees of importance are analyzed. (2) the generalized probabilistic ordered weighted average (ILPOWA) operator of induced language is extended to the generalized probabilistic ordered weighted average (IULGPOWA) operator of induced uncertain language. For the case where the index value is expressed as an uncertain language variable, the IULGPOWA operators formed by the generalized ordered weighted average (IULGOWA) operator of induced uncertain language and the generalized probabilistic weighted average (ULGPWA) operator of uncertain language are used to aggregate. This new integration operator takes into account not only the importance of the location of the parameters, but also the probability of the parameters, which is more general than other integration operators. At the same time, according to the characteristics of the electric power construction project, (3) based on the generalized ordered weighted average (2TLGOWA) operator and the generalized probability weighted average (2TLGPA) operator, the generalized ordered weighted average (2TLIGPOWA) operator is formed. The binary semantics is used to describe the index value, which effectively reduces the loss of information. At the same time, considering the probability information and the attitude characteristics of the decision maker in the generalized environment, a family of aggregation operators is provided in the maximum and minimum values. The generality of the decision model is expounded, which is more general than other integration operators, and an empirical analysis of multi-attribute group decision making of generalized integration operator under the background of electric power construction project is carried out, which verifies its validity. By constructing two kinds of ensemble operators, most of the multi-attribute decision making problems in the environment of description induction language can be solved. At the same time, the examples are provided to prove its feasibility and validity in practice.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O225
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