模糊语言群体多准则决策方法研究
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摘要
决策是人类与生俱来的一项基本能力,人们通过决策活动认识和改造世界。决策广泛存在于人们的日常生产生活活动,大到国家的宏观决策,小到个人行为的决策无不涉及到判断、选择、预测等复杂的认知过程和心智计算。由于决策环境的复杂性和不确定性以及人类思维的不精确性和认知的有限性决定了决策活动的不确定性常态。采用语言形式的信息来描述偏好信息比较符合人类思维的模糊性和不确定性,有利于决策者把握事物的真实状态和减轻决策者的认知压力。因此,基于语言信息的决策研究已成为近年来决策科学界一个新的焦点,得到了突飞猛进的发展,并取得了丰硕的研究成果。但目前的研究无论在理论方法研究还是在实际应用研究尚不完善,值得进一步深入研究。同时,随着社会的发展以及研究的深入,常常需要更加深刻地刻画世界的不确定本质,因而如何发展能更加符合人类思维的不确定性特征的、更加准确反映出决策者真正意愿的非确定(模糊)语言信息来更加真实地反映客观实际情况以及如何将模糊语言信息有效的集成融合以真实反映决策者认知的体现是语言决策极为核心的主题。基于此,本论文针对语言决策开展和完成如下研究工作:
     (1)针对纯语言决策问题,系统研究不同粒度的语言术语集中不同类型的语言信息(异质语言变量)的运算规则,提出用于集结异质语言信息的异质纯语言加权平均算子,异质纯语言加权调和算子,依赖型异质纯语言有序加权平均集结算子以及优先权型异质纯语言加权算子。在此基础上,提出一种简单实用的异质纯语言群体多准则决策方法并通过实例分析该方法的有效性和可行性。
     (2)针对多类型语言决策问题,系统研究各种类型的语言信息(语言变量,不确定语言变量,三角模糊语言变量和梯形模糊语言变量)的语言距离测度,进而,提出一种泛化的融入决策者决策态度的广义语言混合加权距离测度,并分析该距离测度的性质及特例。在系统分析以距离测度思想为代表的TOPSIS决策方法的基础上,利用所提出的广义语言混合加权距离测度,提出一种考虑个体决策态度的群体多类型语言TOPSIS决策方法,该方法主要特点在于考虑了个体决策者的决策个性,即允许采用所偏爱的语言表达信息,允许反应其决策态度,因而降低了决策压力,增加了群体决策的柔性,有利于决策结果的真实性和合理性。同时,利用语言距离测度实现不同类型的语言信息融合。
     (3)针对以两两对比形式表达偏好的不确定语言偏好关系的群体决策问题。研究不确定语言偏好信息的共识性与一致性等基础特性,分别提出不确定语言偏好信息的共识性测度与一致性测度模型。在此基础上提出一种集结不确定语言偏好信息的集结算子,即基于共识性与一致性双重协同诱导的有序加权集结(C2-IULOWA)算子,将偏好信息的共识性能信息与一致性能信息融入到集结算子中参与信息集结,使得集结结果更加科学合理。进而,给出一种基于不确定语言偏好信息的直接群体决策方法。
     (4)针对决策者因模棱两可的主观不确定性,不能采用确切的语言信息进行判断的决策问题。将直觉模糊思想与理论引入语言信息表达,提出直觉模糊语言变量概念,使决策者在使用语言信息更加轻松真实地表达自己的观点的同时,更加细腻地反映决策者认知的不确定性与模棱两可性。进而,基于卢卡西瓦茨三角模和三角余模给出直觉模糊语言变量的基本运算规则,接着提出用于集成直觉模糊语言信息的直觉模糊语言算术加权集结算子,直觉模糊语言有序加权集结算子和直觉模糊语言混合加权集结算子等。此外,将直觉模糊信息熵测度推广到直觉模糊语言环境中得到直觉模糊语言信息的直觉模糊熵和交叉熵测度。最后基于直觉模糊熵和交叉熵测度提出一种考虑准则区分度和准则间相关度的集成定权的直觉模糊语言群体多准则决策方法。
     (5)针对决策者不能简单的用单个语言值来表达其知识而犹豫于多个取值的决策问题。结合模糊集理论最新研究成果犹豫模糊集,提出犹豫模糊语言变量概念,进而提出犹豫模糊语言加权集结算子,犹豫模糊语言有序加权集结算子,犹豫模糊语言混合加权集结算子等。进一步地,研究动态犹豫模糊语言加权平均集结算子。在此基础上提出了一种动态犹豫模糊语言群体多准则决策方法,拓展和丰富了语言决策理论和方法。
     (6)将本研究所得模糊语言决策理论综合应用于电子购物网站的电子服务质量评估问题。首先构造一套较为实用的且方便多消费者群体共同参与的电子质量服务评估体系。并利用其对淘宝、京东商城、亚马逊和凡客四家知名电子购物网站进行多消费者群体共同参与电子服务质量评估。
Decision making is inherent ablities of human and human utlize it to recognize and reform the world. Decision making prevail in our daily works and lives which refer to judgment, selection or prediction etc with our recognizing procedure and mental computing, it include not only the macroscopic decision of nations but also the individual behavioral decision. Due to the facts that complex and uncertain decision environment and the imprecise human mentality and the limitied human perception determine the uncertain normality of decision making activities. The linguistic decision making allows decision makers express their opinions with linguistic valures rather than numerical numbers, which are more approaximting the human perceptions and then both enhance the rationality and qualities of decision making and reduce their cognitive burdens in the assessments process. Thence, the linguistic decision making is becoming a hot research topic in recent years. Although many invaluable and significant contributions have been made, there still are a lot of questions needing been resolved. Meanwhile, with the development of the society and the requirement of further research, characterizing in depth the uncertain essence of world is necessary. So, how to develop some non-deterministic (fuzzy) linguistic information, which can approaximt the uncertain human perceptions and refelect precisely the genuine opinions of decision makers, is a central topic. Based on which, the dissertation conducted and completed the works as follows:
     (1) With respect to the pure linguistic decision making problems, the basic operational laws of linguistic varables which are different types and their values from linguistic terms sets with different granularities are intensively inversitagted, some heterogeneous pure linguistic aggregation operators used to aggregate linguistic information are presented, include the heterogeneous pure linguistic weighted averaging operator, the heterogeneous pure weighted harmonic ageraging operator, the heterogeneous pure linguistic depented ordered weighted averaging operators and the heterogeneous prue linguistic priorities aggregation operator. Then, a heterogeneous pure linguistic group multi-critera decision making method based on the proposed heterogeneous pure linguistic operators is developed to accommodate to the different granular and different types linguistic information. The effectiveness and the praticalness are verified by an illustration example.
     (2) With respect to the multi-types linguistic decision making problems, the linguistic distance measures of different types linguistic variables is investigated, and then some generalized linguistic hybrid weigthed distance mesures between linguistic variables vectors, uncertain linguistic variables vectors, triangular linguistic variables vectors and the trapezoidal linguistic variables vectors are proposed, their some properties and special cases are discussed. On the basis of the systermic analiysis of the TOPSIS decision making methodology, ultilizes the proposed linguistic distance measure, we presented a group multi-types linguistic TOPSIS method which can refelect the individual decision attitudes. The main advantages of the proposed method are that it can permit decisiom makers express freely their individual decision attitudes and provide their opinions with different types of linguistic information and thus allivate the decision pressure and enhance the flexibility of decision making. In addition, the proposed method provides a way to fuse the linguistic information with deifferent types.
     (3) With respect to group decision making problems with uncertain linguistic preference relations, some basic properties of uncertain linguistic preference information such as consensus and sonsistency are invesitegated, a consensus measure and a consistency measure are proposed and their some desirable properties are analized. Based on the results, we presented an aggregation operator to aggregate uncertain preference information, called the consensus and consistency co-induced uncertain linguistic ordered weighted averaging (C2-IULOWA) operator, which merge the consensus and consistency information into the aggregation process and make the aggregated results more scientific and reasonable. Furthermore, we developed a direct group decision making method with uncertain linguistic preference information.
     (4) With respect to the problems that decision makers can not express their opinions with definite linguistic information. Merging the advantages of intuitionistic fuzzy ideas into linguistic variables, we proposed the concepts of intuitionistic fuzzy linguistic variables to more exquisite elicit the uncertainties of the vagueness of decision makers's cognitive process. On the baisis of the Lukasiewicz T-normsand T-cnorms, we defined the basic operational laws of intuitionistic fuzzy linguistic variables. And then, we presented some intuitionistic fuzzy linguistic aggregation operators, such as intuitionistic fuzzy linguistic weighted averaging operator, intuitionistic fuzzy linguistic arithmatical averaging operator, intuitionistic fuzzy linguistic ordered weighted averaging operator, and intuitionistic fuzzy linguistic hybrid weighted averaging operator and disussed briefly their desirable properties. Furthermore, based on the intuitionistic fuzzy entropy measure and the cross-entropy measure, we presented a group multi-criteria decision making method with intuitionistic fuzzy linguistic information, in which the intuitionistic fuzzy linguistic entropy measure and the cross entropy measure is utilized to determined the criteria weights, and the intuitionistic fuzzy linguistic arithmatical averaging operator is used to aggregate the the criteria values and opinions take forms of intuitionistic fuzzy linguistic information, in addition, the effective and practicle of the developed method is verified by a illustration example.
     (5) With respect to the problems that decision makers can only hesitantly express their opinions with multiple linguistic values rather the single linguistic value. Injecting the ideas of hesitant fuzzy into linguistic variables and presented hesitant fuzzy linguistic variables to model the hesitancy and undetermination of the decision makers'opinions, on the basis of the theory of hesitant fuzzy sets, some operational laws of hesitant fuzzy linguistic variables are defined. To synthesize hesitant fuzzy linguistic information, some hesitant fuzzy linguistic aggregation operators are presented. In addition, considering to the fact that hesitant fuzzy linguistic we further proposed some dynamic hesitant fuzzy linguistic aggregation operators. Finally, a dynamic hesitant fuzzy linguistic group multi-cirteria decision making method is developed and verified by a numerical example.
     (6) Comprehensively utilizing the investigated results to the electronic service quality assessment of the electronic business websites, deveopled a practical methodology of electronic service quality assessment which facilitate to the heterogeneous group consist of different custormer goups. Furthermore, the electronic service quality of four China's notable the electronic business websites are assessed by the developed methodology.
引文
[1]ZADEH L. A. Concept of a Linguistic Variable and its Application to Approximate Reasoning-Ⅰ [J]. Information Sciences,1975,8(3):199-249.
    [2]ZADEH L. A. Concept of a Linguistic Variable and its Application to Approximate Reasoning-Ⅱ [J]. Information Sciences,1975,8(4):301-357.
    [3]ZADEH L. A. Concept of a Linguistic Variable and its Application to Approximate Reasoning-Ⅲ [J]. Information Sciences,1975,9(1):43-80.
    [4]GRABISCH M. Fuzzy Integral in Multicriteria Decision Making[J]. Fuzzy Sets and Systems,1995,69(3):279-298.
    [5]CARLSSON C, FULLBR R. Fuzzy Multiple Criteria Decision Making: Recent Developments[J]. Fuzzy Sets and Systems,1996,78(2):139-153.
    [6]CHANG D.Y. Applications of the Extent Analysis Method on Fuzzy AHP[J]. European Journal of Operational Research,1996,95(3):649-655.
    [7]HSU H.-M., CHEN C.-T. Fuzzy Credibility Relation Method for Multiple Criteria Decision-Making Problems[J]. Information Sciences,1997,96(1-2):79-91.
    [8]CHEN C.-T. Extensions of the TOPSIS for Group Decision-Making under Fuzzy Environment[J]. Fuzzy Sets and Systems,2000,114(1):1-9.
    [9]GUH Y.Y., PO R.W., LEE E.S. The Fuzzy Weighted Average Within a Generalized Means Function[J]. Computers and Mathematics with Applications,2008,55(12):2699-2706.
    [10]TAKEDA E. A Method for Multiple Pseudo-Criteria Decision Problems[J]. Computers & Operations Research,2001,28(14):1427-1439.
    [11]MACHARIS C, SPRINGAEL J., BRUCKER K.D., VERBEKE A. PROMETHEE and AHP:The Design of Operational Synergies in Multicriteria Analysis Strengthening PROMETHEE with Ideas of AHP[J]. European Journal of Operational Research,2004,153(2):307-317.
    [12]OZTURK M., TSOUKIAS A. Bipolar Preference Modeling and Aggregation in Decision Support[J]. International Journal of Intelligent Systems,2008,23(9):970-984.
    [13]LI W., LI B. An Extension of the PROMETHEE Ⅱ Method Based on Generalized Fuzzy Numbers[J]. Expert Systems with Applications,2010, 37(7):5314-5319.
    [14]MARIMIN, UMANO M., HATONO I., TAMURA H. Linguistic Labels for Expressing Fuzzy Preference Relations in Fuzzy Group Decision Making[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics,1998,28(2):205-218.
    [15]CHEN C.-T. A Fuzzy Approach to Select the Location of the Distribution Center[J]. Fuzzy Sets and Systems,2001,118(1):65-73.
    [16]LIANG G.S., CHOU T.Y., HAN T.C. Cluster Analysis Based on Fuzzy Equivalence Relation[J]. European Journal of Operational Research,2005, 166(1):160-171.
    [17]KUO M.-S., LIANG G.-S., HUANG W.-C. Extensions of the Multicriteria Analysis with Pairwise Comparison under a Fuzzy Environment[J]. International Journal of Approximate Reasoning,2006,43(3):268-285.
    [18]WANG T. C., CHEN Y.H. Applying Fuzzy Linguistic Preference Relations to the Improvement of Consistency of Fuzzy AHP[J]. Information Sciences, 2008,178(19):3755-3765.
    [19]CHEN Y.H., WANG T. C. Incomplete Fuzzy Linguistic Preference Relations under Uncertain Environments[J]. Information Fusion,2010, 11(2):201-207.
    [20]GHAZINOORY S., ESMAIL ZADEH A., KHEIRKHAH A.S. Application of Fuzzy Calculations for Improving Portfolio Matrices[J]. Information Sciences,2010,180(9):1582-1590.
    [21]WANG Y.J. A Clustering Method Based on Fuzzy Equivalence Relation for Customer Relationship [J]. Expert Systems with Applications,2010,37(9): 6421-6428.
    [22]RAMIK J., KORVINY P. Inconsistency of Pair-wise Comparison Matrix with Fuzzy Elements Based on Geometric Mean[J]. Fuzzy Sets and Systems,2010,161(11):1604-1613.
    [23]LEUNG L.C., CAO D. On Consistency and Ranking of Alternatives in Fuzzy AHP[J]. European Journal of Operational Research,2000,124(1): 102-113.
    [24]CHICLANA F, HERRERA F, HERRERA-VIEDMA E., MARTINEZ L. A Note on the Reciprocity in the Aggregation of Fuzzy Preference Relations Using OWA Operators[J]. Fuzzy Sets and Systems,2003,137(1):71-83.
    [25]SWITALSKI Z. General Transitivity Conditions for Fuzzy Reciprocal Preference Matrices[J]. Fuzzy Sets and Systems,2003,137(1):85-100.
    [26]AGUARON J., MORENO-JIMENEZ J.M. The Geometric Consistency Index:Approximated Thresholds[J]. European Journal of Operational Research,2003,147(1):137-145.
    [27]AGUARON J., ESCOBAR M.T., MORENO-JIMENEZ J.M. Consistency Stability Intervals for a Judgement in AHP Decision Support Systems [J]. European Journal of Operational Research,2003,145(2):382-393.
    [28]CHICLANA F, HERRERA F, HERRERA-VIEDMA E. Rationality of Induced Ordered Weighted Operators Based on the Reliability of the Information Sources for Aggregation for Group Decision-Making[J]. Kybernetica,2004,40(1):121-142.
    [29]ZADEH L. A. Fuzzy Logic Equals Computing with Words[J]. IEEE Transactions on Fuzzy Systems,1996,4(2):103-111.
    [30]DELGADO M., VERDEGAY J. L., VILA M. A. Linguistic Decision-Making Models[J]. International Journal of Intelligent Systems,1992,7(5): 479-492.
    [31]DELGADO M., VERDEGAY J. L., VILA M. A. On Aggregation Operations of Linguistic Labels[J]. International Journal of Intelligent Systems,1993,8(3):351-370.
    [32]HERRERA F. HERRERA-VIEDMA E., VERDEGAY J.L. Direct Approach Processes in Group Decision Making Using Linguistic OWA Operators[J]. Fuzzy Sets and Systems,1996,79(2):175-190.
    [33]HERRERA F., HERRERA-VIEDMA E., VERDEGAY J.L. Choice Processes for Non-Homogeneous Group Dfiflsinn Making in Linguistic, Setting[J]. Fuzzy Sets and Systems,1998,94(3) 287-308.
    [34]CHENG C.H.,YANG K.L.,HWANG C.L. Evaluating Attack Helicopters by AHP Based on Linguistic Variable Weight[J]. European Journal of Operational Research,1999,116(2):423-435.
    [35]HERRERA F, HERRERA-VIEDMA E. Choice Functions and Mechanisms for Linguistic Preference Relations[J]. European Journal of Operational Research,2000,120(1):144-161.
    [36]TORRA V. Aggregation of Linguistic Labels When Semantics is Based on Antonyms[J]. International Journal of Intelligent Systems,2001, 16(4):513-524.
    [37]CARLSSON C, FULLER R. Benchmarking in Linguistic Importance Weighted Aggregations[J]. Fuzzy Sets and Systems,2000,114(1):35-41.
    [38]HERRERA F., MARTINEZ L. A 2-Tuple Fuzzy Linguistic Representation Model for Computing with Words[J]. IEEE Transactions on Fuzzy Systems, 2000,8(6):746-752.
    [39]HERRERA F., HERRERA-VIEDMA E., MARTINEZ L. A Fusion Approach for Managing Multi-Granularity Linguistic Term Sets in Decision Making[J]. Fuzzy Sets and Systems,2000,14(1):43-58.
    [40]HERRERA F., MARTINEZ L. A Model Based on Linguistic 2-Tuples for Dealing with Multigranular Hierarchical Linguistic Contexts in Multi-Expert Decision-Making. IEEE Transactions on Systems, Man, and Cybernetics—Part B:Cybernetics,2001,31(2):227-234.
    [41]XU Z. EOWA and EOWG Operators for Aggregating Linguistic Labels Based on Linguistic Preference Relations[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,2004,12(6):791-810.
    [42]WANG J.H., HAO J. A New Version of 2-Tuple Fuzzy Linguistic Representation Model for Computing with Words[J]. IEEE Transactions on Fuzzy Systems,2006,14(3):435-445.
    [43]HERRERA F., HERRERA-VIEDMA E., MARTINEZ L. A Fuzzy Linguistic Methodology to Deal with Unbalanced Linguistic Term Sets[J]. IEEE Transactions on Fuzzy Systems,2008,16(2):354-370.
    [44]XU Z. An Interactive Approach to Multiple Attribute Group Decision Making with Multigranular Uncertain Linguistic Information [J]. Group Decision and Negotiation,2009,18(2):119-145.
    [45]LI D.F. Multiple Attribute Group Decision Making Method Using Extended Linguistic Variables [J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,2009,17(6):793-806.
    [46]DONG Y., XU Y., LI H., Feng B. The OWA-Based Consensus Operator Under Linguistic Representation Models Using Position Indexes[J]. European Journal of Operational Research,2010,203(2):455-463.
    [47]王欣荣,樊治平.基于二元语义信息处理的一种语言群决策方法[J].管理科学学报,2003,6(5):1-5.
    [48]MARTINEZ L., PEREZ L. G, BARRANCO M. A Multigranular Linguistic Content-Based Recommendation Model[J]. International Journal of Intelligent Systems,2007,22(5):419-434.
    [49]相辉.语言型时序多属性群决策方法及在服务创新中的应用[J].运筹与管理,2009,18(4):44-49.
    [50]WEI GW. A Method for Multiple Attribute Group Decision Making Based on the ET-WG and ET-OWG Operators with 2-Tuple Linguistic Information[J]. Expert Systems with Applications,2010,37(12):7895-7900.
    [51]YANG W., CHEN Z. New Aggregation Operators Based on the Choquet Integral and 2-Tuple Linguistic Information[J]. Expert Systems with Applications,2012,39(3):2662-2668.
    [52]WEI G.W. Some Generalized Aggregating Operators with Linguistic Information and Their Application to Multiple Attribute Group Decision Making[J]. Computers & Industrial Engineering,2011,61(1):32-38.
    [53]DONG Y, XU Y, YU S. Computing the Numerical Scale of the Linguistic Term Set for the 2-Tuple Fuzzy Linguistic Representation Model[J]. IEEE Transactions on Fuzzy Systems,2009,17(6):1366-1378.
    [54]TAI W.-S., CHEN C.-T. A New Evaluation Model for Intellectual Capital Based on Computing with Linguistic Variables[J]. Expert Systems with Applications,2009,36(2):3483-3488.
    [55]RODRIGUEZ R. M., ESPINILLA M., SANCHEZ P. J., MARTFNEZ-LOPEZ L. Using Linguistic Incomplete Preference Relations to Cold Start Recommendations[J]. Internet Research,2010,20(3):296-315.
    [56]DONG Y, HONG W.C., XU Y, YU S. Selecting the Individual Numerical Scale and Prioritization Method in the AHP:A 2-Tuple Fuzzy Linguistic Approach[J]. IEEE Transactions on Fuzzy Systems,2011,19(1):13-25.
    [57]PEREZ I.J., CABRERIZO F.J., HERRERA-VIEDMA E. Group Decision- Making Problems in a Linguistic and Dynamic Context[J]. Expert Systems with Applications,2011,38(3):1675-1688.
    [58]HERRERA-VIEDMA E., MARTINEZ L., MATA F., CHICLANA F. A Consensus Support System Model for Group Decision-Making Problems with Multigranular Linguistic Preference Relations[J]. IEEE Transactions on Fuzzy Systems,2005,13(5):644-658.
    [59]DONG Y., XU Y., LI H. On Consistency Measures of Linguistic Preference Relations[J]. European Journal of Operational Research,2008,189(2): 430-444.
    [60]HALOUANI N., CHABCHOUB H., MARTEL J.-M. PROMETHEE-MD-2T Method for Project Selection[J]. European Journal of Operational Research,2009,195(3):841-849.
    [61]CABRERIZO F.J., PEREZ I.J., HERRERA-VIEDMA E., Managing the Consensus in Group Decision-Making in an Unbalanced Fuzzy Linguistic Context with Incomplete Information[J]. Knowledge-Based Systems,2010, 23(2):169-181.
    [62]CABRERIZO F. J., MORENO J. M., PEREZ I. J., HERRERA-VIEDMA E. Analyzing Consensus Approaches in Fuzzy Group Decision-Making: Advantages and Drawbacks[J]. Soft Computing,2010,14(5):451-463.
    [63]ALONSO S., HERRERA-VIEDMA E., CHICLANA F., HERRERA F. A Web Based Consensus Support System for Group Decision-Making Problems and Incomplete Preferences [J]. Information Sciences,2010, 180(23):4477-4495.
    [64]PARREIRAS R.O., EKEL P.Y., MARTINI J.S.C., PALHARES R.M. A Flexible Consensus Scheme for Multicriteria Group Decision Making under Linguistic Assessments[J]. Information Sciences,2010,180(7): 1075-1089.
    [65]XU Z. A Method Based on Linguistic Aggregation Operators for Group Decision-Making with Linguistic Preference Relations[J]. Information Sciences,2004,166(1-4):19-30.
    [66]XU Z. On Generalized Induced Linguistic Aggregation Operators[J]. International Journal of General Systems,2006,35(1):17-28.
    [67]XU Z. An Interactive Procedure for Linguistic Multiple Attribute Decision Making with Incomplete Weight Information[J]. Fuzzy Optimization and Decision Making,2007,6(1):17-27.
    [68]KIM S.H., AHN B.S. Interactive Group Decision Making Procedure under Incomplete Information[J]. European Journal of Operational Research, 1999,116(3):498-507.
    [69]XU Z. A Method for Multiple Attribute Decision Making with Incomplete Weight Information in Linguistic Setting[J]. Knowledge-Based Systems, 2007,20(8):719-725.
    [70]WU Z., CHEN Y. The Maximizing Deviation Method for Group Multiple Attribute Decision Making under Linguistic Environment[J]. Fuzzy Sets and Systems,2007,158(14):1608-1617.
    [71]XU Z. Multi-Period Multi-Attribute Group Decision-Making under Linguistic Assessments [J]. International Journal of General Systems,2009, 38(8):823-850.
    [72]朱卫东,周光中,杨善林.基于二维语言评价信息的群体决策方法[J].系统工程,2009,27(2):113-118.
    [73]MERIGO J.M., GIL-LAFUENTE A.M.,ZHOU L.G., et al. Induced and Linguistic Generalized Aggregation Operators and Their Application in Linguistic Group Decision Making[J] Group Decision and Negotiation, DOI 10.1007/s10726-010-9225-3.
    [74]谭春桥,马本江.基于语言Choquet积分算子的多属性群决策方法[J].系统工程与电子技术,2010,32(11):2352-2355.
    [75]ZHOU L., CHEN H. A Generalization of the Power Aggregation Operators for Linguistic Environment and Its Application in Group Decision Making[J]. Knowledge-Based Systems,2012,26:216-224.
    [76]MERIGO J.M., GIL-LAFUENTE A.M., ZHOU L.G., et al. Generalization of the Linguistic Aggregation Operator and Its Application in Decision Making[J]. Journal of Systems Engineering and Electronics.2011. 22(4):593-603.
    [77]XU Z. Deviation Measures of Linguistic Preference Relations in Group Decision-Making[J]. Omega,2005,33(3):249-254.
    [78]HSU S.C., WANG T.C. Solving MCDM with Incomplete Linguistic Preference Relations[J]. Expert Systems with Applications,2011,38(9): 10882-10888.
    [79]XU Z. Uncertain Linguistic Aggregation Operators Based Approach to Multiple Attribute Group Decision Making under Uncertain Linguistic Environment[J]. Information Sciences,2004,168(1-4):171-184.
    [80]徐泽水.纯语言多属性群决策方法研究[J].控制与决策,2004,19(7):778-781.
    [81]XU Z. An Approach to Pure Linguistic Multiple Attributes Decision Making under Uncertainty[J]. International Journal of Information Technology & Decision Making,2005,4(2):197-206.
    [82]XU Z. An Approach Based on Similarity Measure to Multiple Attribute Decision Making with Trapezoid Fuzzy Linguistic Variables[J]. Lecture Notes in Artificial Intelligence,2005,3613:110-117.
    [83]XU Z. Induced Uncertain Linguistic OWA Operators Applied to Group Decision Making[J]. Information Fusion,2006,7(2):231-238.
    [84]XU Z. Group Decision Making with Triangular Fuzzy Linguistic Variables[J]. Lecture Notes in Computer Science,2007,4881:17-26.
    [85]MERIGO J.M., GIL-LAFUENTE A.M., MARTINEZ L. Linguistic Aggregation Operators for Linguistic Decision Making Based on the Dempster-Shafer Theory of Evidence[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,2010,18(3):287-304.
    [86]LIU P., SU Y. The Multiple-Attribute Decision Making Method Based on the TFLHOWA Operator[J]. Computers and Mathematics with Applications,2010,60(9):2609-2615.
    [87]乐琦,樊治平.具有多粒度不确定语言评价信息的多属性群决策方法[J].控制与决策,2010,25(7):1059-1062.
    [88]王坚强,李寒波.基于直觉语言集结算子的多准则决策方法[J].控制与决策,2010,25(10):1571-1574.
    [89]CHEN S.M., LEE L.W. Autocratic Decision Making Using Group Recommendations Based on the ILLOWA Operator And Likelihood-Based Comparison Relations[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part A:Systems and Humans,2012,42(1):115-129.
    [90]PARK J. H., GWAK M. G, KWUN Y. C. Uncertain Linguistic Harmonic Mean Operators and Their Applications to Multiple Attribute Group Decision Making[J]. Computing,2011,93(1):47-64.
    [91]LIU P., JIN F, ZHANG X.,et al. Research on the Multi-Attribute Decision-Making under Risk with Interval Probability Based on Prospect Theory and the Uncertain Linguistic Variables[J]. Knowledge-Based Systems,2011,24 (4):554-561.
    [92]陈俊良,刘新建,陈超.基于语言决策矩阵的专家客观权重确定方法[J].系统工程与电子技术,2011,33(6):1310-1316.
    [93]刘培德,张新.一种基于区间灰色语言变量几何加权集成算子的多属性群决策方法[J].控制与决策,2011,26(5):743-745.
    [94]王晓,陈华友,刘兮.基于离差的区间二元语义多属性群决策方法[J].管理学报,2011,8(2):301-305.
    [95]张尧,樊治平.基于风险态度因子的不确定语言多指标决策方法[J].东北大学学报(自然科学版),2011,32(6):883-886.
    [96]XU Z. A Direct Approach to Group Decision-Making with Uncertain Additive Linguistic Preference Relations[J]. Fuzzy Optimization and Decision Making,2006,5(1):21-32.
    [97]XU Z. An Approach Based on the ULOWG and IULOWG Operators to Group Decision-Making with Uncertain Multiplicative Linguistic Preference Relations[J]. Decision Support Systems,2006,41(2):488-499.
    [98]XU Z. Interactive Group Decision-Making Procedure Based on Uncertain Multiplicative Linguistic Preference Relations[J]. Journal of Systems Engineering and Electronics,2010,21(3):408-415.
    [99]XU Z. Group Decision Making Based on Multiple Types of Linguistic Preference Relations[J]. Information Sciences,2008,178(2):452-467.
    [100]CHEN H., ZHOU L., HAN B. On Compatibility of Uncertain Additive Linguistic Preference Relations and Its Application in the Group Decision-Making[J].Knowledge-Based Systems ,2011,24(6);816-823.
    [101]ALONSO S., CHICLANA F., HERRERA F., HERRERA-VIEDMA E., ALCALA-FDEZ J., PORCEL C. A Consistency-Based Procedure to Estimate Missing Pairwise Preference Values[J]. International Journal of Intelligent Systems,2008,23(2):155-175.
    [102]ZHANG Y., FAN.Z. Method for Multiple Attribute Decision Making Based on Incomplete Linguistic Judgment Matrix[J]. Journal of Systems Engineering and Electronics,2008,19(2):298-303.
    [103]FAN.Z.-R, FENG B. A Multiple Attributes Decision Making Method Using Individual and Collaborative Attribute Data in A Fuzzy Environment[J]. Information Sciences,2009,179(20):3603-3618.
    [104]ZHANG Z., CHU X. Fuzzy Group Decision-Making for Multi-Format and Multi-Granularity Linguistic Judgments in Quality Function Deployment[J]. Expert Systems with Applications,2009,36(5):9150-9158.
    [105]姜艳萍,梁海明.一种基于粗集的残缺语言区间信息的多属性群决策方法[J].系统管理学报,2011,20(4):485-489.
    [106]朱建军,刘思峰,田飞.群决策中三端点语言和互补偏好信息的转化及集结研究[J].中国管理科学,2011,19(3):141-147.
    [107]ATANASSOV K.T. Intuitionistic Fuzzy Sets[J]. Fuzzy Sets and Systems, 1986,20(1):87-96.
    [108]ATANASSOV K., GARGOV G. Interval Valued Intuitionistic Fuzzy Sets [J]. Fuzzy Sets and Systems,1989,31(3):343-349.
    [109]ATANASSOV K.T. New Operations Defined Over The Intuitionistic Fuzzy Sets[J]. Fuzzy Sets and Systems,1994,61(2):137-142.
    [110]ATANASSOV K.T. Operators over Interval Valued Intuitionistic Fuzzy Sets [J]. Fuzzy Sets and Systems,1994,64(2):159-174.
    [111]DESCHRIJVER D. Quasi-Arithmetic Means and OWA Functions in Interval-Valued and Atanassov's Intuitionistic Fuzzy Set Theory[C] EUSFLAT-LFA,2011,506-513.
    [112]BELIAKOV G., BUSTINCE H, GOSWAMI D.P., MUKHERJEE U.K., PAL N.R. On Averaging Operators for Atanassov's Intuitionistic Fuzzy Sets[J]. Information Sciences,2011,181(6):1116-1124.
    [113]BELIAKOV G, BUSTINCE H, JAMES S., CALVO T., FERNANDEZ J. Aggregation for Atanassov's Intuitionistic and Interval Valued Fuzzy Sets: the Median Operator[J]. IEEE Transactions on Fuzzy Systems,2012, DOI10.1109/TFUZZ.2011.2177271.
    [114]TORRA V. Element Selection for Intuitionistic Fuzzy Sets[J] International Journal of Knowledge Engineering and Soft Data Paradigms,2010,2(2): 160-168.
    [115]YAGER R.R. OWA Aggregation of Intuitionistic Fuzzy Sets[J] International Journal of General Systems,2009,38(6):617-641.
    [116]YAGER R.R. Some Aspects of Intuitionistic Fuzzy Sets[J] Fuzzy Optim Decis Making,2009,8(1):67-90.
    [117]LIU H.-W., WANG G.J. Multi-Criteria Decision-Making Methods Based on Intuitionistic Fuzzy Sets[J] European Journal of Operational Research, 2007,179(1):220-233.
    [118]XU Z., YAGER R.R. Some Geometric Aggregation Operators Based on Intuitionistic Fuzzy Sets[J]. International Journal of General Systems,2006, 35(4):417-433.
    [119]XU Z. Intuitionistic Fuzzy Aggregation Operators[J] IEEE Transactions on Fuzzy Systems,2007,15(6):1179-1187.
    [120]ZHAO H., XU Z., NI M., Generalized Aggregation Operators for Intuitionistic Fuzzy Sets[J]. International Journal of Intelligent Systems, 2010,25(1):1-30.
    [121]KACPRZYK J., ZADROZNY S. How to Support Consensus Reaching Using Action Rules:A Novel Approach[J] International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,2010,18(4):451-470.
    [122]BURILLO P., BUSTINCE H. Entropy on Intuitionistic Fuzzy Sets and on Interval-Valued Fuzzy Sets[J]. Fuzzy Sets and Systems,1996,78(3):305-316.
    [123]VLACHOS I. K., SERGIADIS G. D. Intuitionistic Fuzzy Information-Applications to Pattern Recognition[J] Pattern Recognition Letters,2007, 28(2):197-206.
    [124]WANG W., LIU X. Intuitionistic Fuzzy Geometric Aggregation Operators Based on Einstein Operations[J]. International Journal of Intelligent-Systems,2011,26(11):1049-1075.
    [125]XIA M., XU Z. Entropy/Cross Entropy-Based Group Decision Making under Intuitionistic Fuzzy Environment[J]. Information Fusion,2012, 13(1):31-47.
    [126]TORRA V. Hesitant Fuzzy Sets[J]. International Journal of Intelligent Systems,2010,25(6):529-539.
    [127]TORRA V., NARUKAWA Y. On Hesitant Fuzzy Sets and Decision[C].The 18th IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 2009,1378-1382.
    [128]RODRIGUEZ R. M., MARTINEZ L., HERRERA F. Hesitant Fuzzy Linguistic Term Sets for Decision Making[J]. IEEE Transactions on Fuzzy Systems,2012,20(1):109-119.
    [129]XIA M., XU Z. Hesitant Fuzzy Information Aggregation in Decision Making[J]. International Journal of Approximate Reasoning,2011,52(3): 395-407.
    [130]XIA M., XU Z. CHEN N. Some Hesitant Fuzzy Aggregation Operators with Their Application in Group Decision Making[J]. Group Decision and Negotiation, DOI:10.1007/s10726-011-9261-7.
    [131]XIA M., XU Z. CHEN N. Induced Aggregation under Confidence Levels[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,2011,19(2):201-227.
    [132]XU Z., XIA M. On Distance and Correlation Measures of Hesitant Fuzzy Information[J]. International Journal of Intelligent Systems,2011,26(5): 410-425.
    [133]XU Z., XIA M. Distance and Similarity Measures for Hesitant Fuzzy Sets[J]. Information Sciences,2011,181(11):2128-2138.
    [134]TORRA V., NARUKAWA Y. Modeling Decisions:Information Fusion and Aggregation Operators[M].Berlin:Springer,2007:147-191.
    [135]YAGER R.R. On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decision-Making[J]. IEEE Transactions on Systems, Man, and Cybernetics,1988,18(1):183-190.
    [136]YAGER R.R. Quantifier Guided Aggregation Using OWA Operators[J]. International Journal of Intelligent System,1996,11(1):49-73.
    [137]YAGER R.R. Families of OWA Operators[J]. Fuzzy Sets and Systems, 1993,59:125-148.
    [138]XU Z. An overview of Methods for Determining OWA Weights[J]. International Journal of Intelligent Systems,2005,20(8):843-865.
    [139]YAGER R. R., FILEV D. P. Induced Ordered Weighted Averaging Operators[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part B:Cybernetics,1999,29(2):141-150.
    [140]XU Z. Dependent OWA Operators[J]. Lecture Notes in Artificial Intelligence,2006,3885:172-178.
    [141]YAGER R.R. Modeling Prioritized Multicriteria Decision Making[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part B:Cybernetics, 2004,34(6):2396-2404.
    [142]YAGER R.R. Prioritized Aggregation Operators[J]. International Journal of Approximate Reasoning,2008,48(1):263-274.
    [143]LUMMUS R.R., DUCLOS L.K., VOKURKAR.J. Supple Chain Flexibility: Building a New Model [J]. Global Journal of Flexible Systems Management,2003,4 (4):1-13.
    [144]LUMMUS R.R., VOKURKA R.J., DUCLOS L.K. Delphi Study on Supple Chain Flexibility [J]. International Journal of Production Research,2005, 43(13):2687-2708.
    [145]ENGEMANN K. J., FILEV D. P., YAGER R. R., Modelling Decision Making Using Immediate Probabilities[J]. International Journal of General Systems,1996,24(3):281-294.
    [146]BAUCELLS M., SARIN R. K. Group Decisions with Multiple Criteria[J]. Management Science,2003,49(8):1105-1118.
    [147]DYCKHOFF H., PEDRYCZ W. Generalized Means as Model of Compensative Connectives[J]. Fuzzy Sets and Systems,1984,14(2):143— 154.
    [148]XU Z., CHEN J. Ordered Weighted Distance Measure[J]. Journal of Systems Science and Systems Engineering,2008,17(4):432-445.
    [149]MERIGO J.M., CASANOVAS M., A New Minkowski Distance Based on Induced Aggregation Operators[J]. International Journal of Computational Intelligence Systems,2011,4(2):123-133.
    [150]LAT Y-J, LIU T-Y., HWANG C.-T,. TOPSIS for MODM[J] EUrOpeAn. Journal of Operational Research,1994,76(3):486-500.
    [151]LIN Y.-H., LEE P.-C, CHANG T.-R, TING H.-I, Multi-Attribute Group Decision Making Model Under The Condition Of Uncertain Information[J]. Automation in Construction,2008,17(6):792-797.
    [152]TANINO T. Fuzzy Preference Orderings in Group Decision Making[J]. Fuzzy Sets and Systems,1984,12(2):117-131.
    [153]ALONSO S., CHICLANA F, HERRERA F, HERRERA-VIEDMA E. ALCALA-FDEZ J., PORCEL C. A Consistency-Based Procedure to Estimate Missing Pairwise Preference Values[J]. International Journal of Intelligent Systems,2008,23(2):155-175.
    [154]CHICLANA F, HERRERA-VIEDMA E., HERRERA F, ALONSO S. Some Induced Ordered Weighted Averaging Operators and Their Use for Solving Group Decision-Making Problems Based on Fuzzy Preference Relations[J]. European Journal of Operational Research,2007,182(1): 383-399.
    [155]WU J., CAO Q., ZHANG J. An ILOWG Operator Based Group Decision Making Method and its Application to Evaluate the Supplier Criteria[J]. Mathematical and Computer Modelling,2011,54(1-2):19-34.
    [156]WANG Y.-M., ELHAG T.M.S. A Goal Programming Method for Obtaining Interval Weights from an Interval Comparison Matrix[J]. European Journal of Operational Research,2007,177(1):458-471.
    [157]MITCHELL H. B., SCHAEFER P. A. Multiple Priorities in an Induced Ordered Weighted Averaging Operator [J]. International Journal of Intelligent Systems,2000,15(4):317-327.
    [158]XU Z., YAGER R.R. Dynamic Intuitionistic Fuzzy Multi-Attribute Decision Making[J]. International Journal of Approximate Reasoning, 2008,48(1):246-262.
    [159]YAGER R.R. Time Series Smoothing and OWA Aggregation[J] IEEE Transactions on Fuzzy Systems,2008,16(4):994-1007.
    [160]YANG Z., FANG X. Online Service Quality Dimensions and Their Relationships with Satisfaction:A Content Analysis of Customer Reviews of Securities Brokerage Services [J]. International Journal of Service Industry Management,2004,15(4):302-326.
    [161]PARASURAMAN A., ZEITHAML V. A., MALHOTRA A. E-S-QUAL:A Multiple-Item Scale for Assessing Electronic Service Quality [J]. Journal of Service Research,2005,7(3):213-233.
    [162]KIM M., KIM J. H., LENNON S. J. Online Service Attributes Available On Apparel Retail Web Sites:an E-S-QUAL Approach[J]. Managing Service Quality,2006,16(1):51-77.