This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance.It is called the induced heavy ordered weighted averaging（OWA） dista...This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance.It is called the induced heavy ordered weighted averaging（OWA） distance（IHOWAD） operator.This paper studies some of its main properties and a wide range of particular cases such as the induced heavy OWA（IHOWA） operator,the induced OWA distance（IOWAD） operator and the heavy OWA distance（HOWAD） operator.This approach is generalized by using generalized and quasi-arithmetic means obtaining the induced generalized IHOWAD（IGHOWAD） operator and the Quasi-IHOWAD operator.An application of the new approach in a decision making problem regarding the selection of strategies is developed.展开更多
A generalization of the linguistic aggregation functions （or operators） is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean （LWGM） and the linguistic gene...A generalization of the linguistic aggregation functions （or operators） is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean （LWGM） and the linguistic generalized ordered weighted averaging （LGOWA） operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average （WA） and in the ordered weighted averaging （OWA） function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean （LGM）, the linguistic OWA （LOWA） operator and the linguistic or- dered weighted quadratic averaging （LOWQA） operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.展开更多
The classic data envelopment analysis（DEA） model is used to evaluate decision-making units＇（DMUs） efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new research...The classic data envelopment analysis（DEA） model is used to evaluate decision-making units＇（DMUs） efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output（IO） criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging（OWA） operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.展开更多
基金supported by the projects JC2009-00189 and A/023879/09 from the Spanish Ministry of Science and Innovation
文摘This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance.It is called the induced heavy ordered weighted averaging（OWA） distance（IHOWAD） operator.This paper studies some of its main properties and a wide range of particular cases such as the induced heavy OWA（IHOWA） operator,the induced OWA distance（IOWAD） operator and the heavy OWA distance（HOWAD） operator.This approach is generalized by using generalized and quasi-arithmetic means obtaining the induced generalized IHOWAD（IGHOWAD） operator and the Quasi-IHOWAD operator.An application of the new approach in a decision making problem regarding the selection of strategies is developed.
基金supported by the Spanish Ministry of Education(JC2009-00189)the Spanish Ministry of Foreign Affairs(A/023879/09)+1 种基金the National Natural Science Foundation of China(71071002)Academic Innovation Team of Anhui University(KJTD001B,SKTD007B)
文摘A generalization of the linguistic aggregation functions （or operators） is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean （LWGM） and the linguistic generalized ordered weighted averaging （LGOWA） operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average （WA） and in the ordered weighted averaging （OWA） function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean （LGM）, the linguistic OWA （LOWA） operator and the linguistic or- dered weighted quadratic averaging （LOWQA） operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.
基金supported by the National Natural Science Foundation of China(71471087)
文摘The classic data envelopment analysis（DEA） model is used to evaluate decision-making units＇（DMUs） efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output（IO） criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging（OWA） operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.
