Study On Methods Of Hesitant Fuzzy Multiple Attribute Group Decision Making With Completely Unknown Attribute Weights Information

The multiple attribute group decision making problems exist in many fields,such as economy,management,social life and so on.The choices or decisions made by individuals or enterprises all involve the decision making process,and scientific decision making is of great significance to the development of human society.However,in the actual decision making process,due to the increasing complexity of social and economic problems,the limitation of people's cognitive ability and the restriction of technical conditions,which make the environment of decision making complex,it is difficult to get the so-called "complete information".In other words,uncertainty systems exist everywhere.Therefore,in this thesis,for the hesitant fuzzy multiple attribute group decision making problem with completely unknown attribute weights,such as the determination of attribute weights,the information aggregation and so on,the following two aspects are carried out:(1)Aiming at the problem that artificial addition of elements in hesitant fuzzy elements affects the results of the decision making,a group decision making method of hesitant fuzzy prospect theory based on improved signed distance is proposed.Firstly,the entropy weight method is used to determine the attribute weights,a new signed distance is defined according to the positive and negative ideal points,the variances of hesitant fuzzy elements and the number of elements,and it is proved that the improved signed distance satisfies the basic properties of signed distance.Furthermore,the hesitant fuzzy prospect value function based on the improved signed distance is proposed,the hesitant fuzzy decision matrix is transformed into the value matrix,and then the profit-loss ratio of each scheme is calculated,and the order of the alternatives is determined.Finally,the feasibility and validity of the proposed method are verified by numerical examples,which demonstrate that this method not only avoids the influence of artificial adding elements on decision making results,but also reflects reasonably the degree of divergence of decision makers.It also considers the psychological behavior of people facing gains and losses,and the results of the decision making are more in line with the actual situation.(2)Aiming at the multiple attribute group decision making problems in which attribute values take the form of hesitant triangular fuzzy information,firstly,the hesitant triangular fuzzy hesitancy index is defined based on the deviation,the supremum and infimum and the number of triangular fuzzy numbers in the triangular fuzzy element,and then the hesitant triangular fuzzy signed distance is defined,and their properties are proved respectively.Then,an attribute weight determination model is constructed based on the hesitant triangular fuzzy signed distance and the maximizing deviation method.Next,the improved hesitant triangular fuzzy weighted average(IHTFWA)operator is developed taking the HTFWA operator and managers' preferred psychological behaviors into account.Finally,a multiple attribute group decision making method based on the hesitant triangular fuzzy signed distance and the IHTFWA operator is proposed,and the feasibility and effectiveness of this method are verified by several illustrative examples.It provides a new method of aggregation and solution for the hesitant triangular fuzzy decision making problems.In this thesis,the hesitant fuzzy multiple attribute group decision making problems with completely unknown attribute weights information is studied,in which the hesitant fuzzy signed distance is improved and new hesitant fuzzy prospect value function is constructed to sort the schemes,the hesitant fuzzy triangular signed distance is defined to determine the attribute weights,and the HTFWA operator is improved to aggregate the attribute information.Several application examples demonstrate that these methods can effectively solve the hesitant fuzzy multiple attribute group decision making problems with completely unknown attribute weights information,and the results of decision making are more realistic and practical.