Methods for the Construction of Membership Functions

Sancho-Royo, Alejandro; Verdegay, José L.

doi:10.1002/(sici)1098-111x(199912)14:12<1213::aid-int3>3.0.co;2-5

Cited by 25 publications

(13 citation statements)

References 21 publications

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“…In particular, the biobjective optimization function is to maximize the matching degree and trading volume. The difference between Jiang's work and our work is that a broker in our approach uses the direct rating (point estimation) method [11] to communicate with buyers to determine buyer's membership function before a broker carries out the matching process between buyers and sellers. Thus, our approach is to maximize buyer's total utility through a broker based on buyer's membership function while Jiang et al [7] does not pay attention to buyer's utility from its membership function in Jiang's bi-objective optimization function.…”

Section: Related Workmentioning

confidence: 99%

“…Step 1: A broker receives b i 's product requirements in fuzzy preference information and s j 's offers in terms of its attributes. A broker determines buyer's membership function for each attribute by using the direct rating (point estimation) method [11]. In this method, a broker communicates with buyers to determine buyer's preference point through questions.…”

Section: Building the Calculation Of Buyer's Utilitymentioning

confidence: 99%

“…where the objective optimization function in Equation (10) seeks to maximize the weight sum of buyer's utility, constraints (11) and (12) are that each buyer…”

Section: Building An Objective Optimization Functionmentioning

confidence: 99%

See 2 more Smart Citations

Membership Function Based Matching Approach of Buyers and Sellers Through a Broker in Open E-Marketplace

Zhang

Ren

2016

Studies in Computational Intelligence

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A broker in a market enables buyers and sellers to do business with each other and can provide many value-adding functions that cannot be replaced by direct buyer-seller dealings. Recently, some research has focused on this issue. However, broker modelling based on buyer's membership functions to carry out a matching process between buyer's requirements in fuzzy preference information and seller's offers is still sparse. Thus, this paper proposes membership function based matching approach of buyers and sellers through a broker in open emarketplace. The major contributions of this paper are that (i) a proposed framework is applicable to help a broker to carry out the matching process between buyers and sellers; (ii) a proposed method is to determine buyer's soft attribute weight by using association rule mining; and (iii) an objective optimization function and a set of constraints are built to help a broker to maximize buyer's total utility. Experimental results demonstrate the good performance of the proposed approach in terms of satisfying buyer's requirements and maximizing buyer's total utility.

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Section: Related Workmentioning

confidence: 99%

Section: Building the Calculation Of Buyer's Utilitymentioning

confidence: 99%

See 1 more Smart Citation

Membership Function Based Matching Approach of Buyers and Sellers Through a Broker in Open E-Marketplace

Zhang

Ren

2016

Studies in Computational Intelligence

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“…To construct a membership function the negotiator may use one of the following methods (Bilgiç, Turksen, 2000;Chameau, Santamarina, 1987;Sancho-Royo, Verdegay, 1999):…”

Section: Multiple-stage Decision-making Problems Based On the Minimummentioning

confidence: 99%

The Multi-Criteria Negotiation Analysis Based on the Membership Function

Roszkowska

Wachowicz

2014

Studies in Logic, Grammar and Rhetoric

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In this paper we propose a multi-criteria model based on the fuzzy preferences approach which can be implemented in the prenegotiation phase to evaluate the negotiations packages. The applicability of some multi-criteria ranking methods were discussed for building a scoring function for negotiation packages. The first one is Simple Additive Weighting (SAW) technique which determines the sum of the partial satisfactions from each negotiation issue and aggregate them using the issue weights. The other one is Distance Based Methods (DBM), with its extension based on the distances to ideal or anti-ideal package, i.e. the TOPSIS procedure. In our approach the negotiator's preferences over the issues are represented by fuzzy membership functions and next a selected multi-criteria decision making method is adopted to determine the global rating of each package. The membership functions are used here as the equivalents of utility functions spread over the negotiation issues, which let us compare different type of data. One of the key advantages of the approach proposed is its usefulness for building a general scoring function in the ill-structured negotiation problem, namely the situation in which the problem itself as well as the negotiators preferences cannot be precisely defined, the available information is uncertain, subjective and vague. Secondly, all proposed variants of scoring functions produce consistent rankings, even though the new packages are added (or removed) and do not result in rank reversal.

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“…For fuzzy concepts and relations we transform the linguistic expressions of their relevant assertions into fuzzy degrees by following the methods defined in [3]. On the other hand, for vague datatype terms we derive the set of values that have been characterized with this term in the collected feedback and we determine the membership function of the fuzzy set that best describes this term by means of regression analysis on the value-strength pairs.…”

Section: Figure 1: Dialogue Game For Feedback Elicitationmentioning

confidence: 99%