In this paper, we study the fuzzy multi attribute decision making problems with preference information on alternatives, in which the attribute values and the preference values given by the decision maker(s) are in the form of triangular fuzzy numbers, and the information about attribute weights is incomplete. We follow fuzzy multi-attribute decision making problems for online match making process. Many online systems that are available in Internet are lacking many features for match making process. Many of the online match making websites couldn't select a perfect match because they follow simple DSS (Decision Support System). In order to overcome this problem, we propose the fuzzy logic based multi attribute decision making process for the online match making process, which is based on fuzzy set theory. First of all, various criteria are considered for online match making process. Second, the different criteria will be given fuzzy weight terms such as very low, low, medium, high, very high depending upon the importance given by the user.
[...] Online FDM System model is explained in the following figure. (Fig. fuzzy query processing techniques for fuzzy database systems, International Journal of Fuzzy systems, Vol.5, pp. 161-170,2003. K. Zhang and R. Needham. A Private Matchmaking Protocol. http://citeseer.nj.nec.com/71955.html Figure 2.Screen for setting ideal partner criteria REFERENCES L.A.Zadeh, Fuzzy Sets, Information and Control, Vol.8, pp. 338-353,1965. Baldwin and Gramlich ,Cryptographic Protocol for Trustable Match [...]
[...] Using the sets of the preference ratings, certain ratings are assigned to the decision criteria respectively by the decision-maker. After the assignment of the ratings, the membership function will be matched to each rating for the fuzzy arithmetic operation In our method, the triangular fuzzy numbers are used as membership functions corresponding to the elements in term set. The reason of using triangular fuzzy number is that it is easy to be used by the decision-maker. The triangular fuzzy number is denoted as follows: membership set for the criterion C1 be Fi, Fi) of any applicant, FSW value Xi for the criterion C1 is calculated as the following: Xi = b1, c1) ( Fi, Fi, Fi b1, c1) For finding out the CFV for n criteria, the following formula can be applied CFV = F i i n i i n i Substituting Fi and Wi with triangular fuzzy numbers, we can calculate CFV triangular value all the applicants for the particular post they applied. [...]
[...] In Baldwin and Gramlich provided a solution for on-line matchmaking intended to support anonymity of users (i.e., protecting KeyWords: Fuzzy decision making method; linguistic variables; fuzzy set. I.INTRODUCTION Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decisionmaking activities. company and job seekers' identities), authentication of matches, and joint notification to users only in the event of a positive match (i.e., a male's identity is authenticated to the female's identity and viceversa only when both the opposite sex users agree). [...]
[...] The applicability of this method was demonstrated through the Fuzzy Decision Making model to select the most appropriate match for the specific registered users. In this proposed FDM system, there is no limit on the number of the decision criteria and the complexity of the analysis is not greatly affected by the numbers of the decision criteria. Moreover, evaluation of the decision criteria is generally easier than other method since the linguistic variables that are similar to everyday words are used. [...]
[...] But we propose the fuzzy logic based multi-attribute decision making process for the online match making process, which is based on fuzzy set theory. III. BASIC CONCEPTS OF A FUZZY SETS The theory of fuzzy sets was proposed by Zadeh in 1965 Let U be the universe of discourse, U = u un A fuzzy set N in the universe of discourse U can be represented by N = fN (ui ) i n A fuzzy number is a fuzzy subset in the universe of discourse U that is both convex and normal In the following, I introduce the simplified arithmetic operations of triangular fuzzy numbers Let E and L be two triangular fuzzy numbers, where E = b1, c1) L = ( a b c 2 ) Fuzzy Numbers Addition : E L = b1, c1) b c = (a1 + a b1 + b c1 + c 2). [...]
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