Aggregation of fuzzy preference relations to multicriteria decision making

Peneva, Vania; Попчев, Иван

doi:10.1007/s10700-007-9018-6

Cited by 25 publications

(9 citation statements)

References 31 publications

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“…Index j represents the players in C, including the player i when i ∈ C. If i / ∈ C, then X (i, C) = 0. The fuzzy logic system used in this model is the Probabilistic Fuzzy Logic (Peneva and Popchev 2006). The operators of this system are:…”

Section: Definitionmentioning

confidence: 99%

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Fuzzy Negotiation Solutions of n-Person Cooperative Games by Knowledge Engineering in Bargaining

2011

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A model for negotiation is developed upon the basis of a previous model called Fuzzy Negotiation Solution by Knowledge Engineering. The new model, called Compensatory Negotiation Solution by Knowledge Engineering and FNSKE are based on the combination of knowledge of experts in negotiation, rather than on classical notions of rationality. The experts in negotiation present four propositions in a nonmathematical way and with a natural language, according to the theory of Knowledge Engineering. In the CNSKE model, knowledge is represented through logic predicates, and the calculations are made using the Compensatory Fuzzy Logic system. CFL is a system whose operators satisfy the axioms of Utility. The CFL operators, especially because of the compensation property, are more adequate than the norm and co-norm's operators to model human decision-making, according to empirical results. The Good Deal Index in CNSKE is statistically estimated from the GDI in FNSKE. This is a quantitative index, which provides the solution concept. The fuzzy function f (C) represents the likelihood the players of a coalition C to reach an agreement into this coalition. Counterpart Convenience Indexes 1, 2 allow each player to select the best coalition for negotiation. The advantage of CNSKE over FNSKE is that the 123 134 E. González et al. idempotency of conjunction and disjunction operators give every membership function -obtained from the predicates-the possibility to be interpreted by itself. Hence, the truth-values of the CNSKE membership functions can be semantically interpreted. In addition, CNSKE can be easily applied to solve real negotiation problems.

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Section: Definitionmentioning

confidence: 99%

“…Equation (3) is used as a strict fuzzy order: This fuzzy order satisfies the property of fuzzy max-max transitivity (Peneva and Popchev 2006) and the property of fuzzy reciprocity (Chiclana et al 2004), see (Espín et al 2007).…”

Section: Definitionmentioning

confidence: 99%

Fuzzy Negotiation Solutions of n-Person Cooperative Games by Knowledge Engineering in Bargaining

2011

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“…Our map matching algorithm is based on reference [14], but in order to obtain more abundant amount of information and more effective map matching to improve accuracy, we add the following two factors, the direction angle and the running distance, the similarity degree is measured in terms of fuzzy theory, then a fuzzy preference relations is used to the multicriteria decision system [15,16] . We regard the road with the shape of the highest similarity is the road on which the vehicle phone is moving (For details, see reference [17]).…”

Section: B Map Matching Levelmentioning

confidence: 99%

Apply Cellular Wireless Location Technologies to Traffic Information Gathering

Guo

Luo

2009

2009 Second International Conference on Intelligent Computation Technology and Automation

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cellular position is an emerging position location technology to provide information for use in traffic data gathering, the main issue is how to use the cellular geographical coordinates to estimate the traffic information, we used a hierarchical structure to achieve it, the methods of cellular phone classification, map matching and cellular phone clustering were studied. At last, the RBF network was used to adjust traffic flow for vehicles without cell phones.

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“…Vania [21] [22] uses composition of fuzzy relations to aggregate preference relations into a collective one. Concept of fuzzy majority using a linguistic quantifier to aggregate fuzzy preference relation is used by Tanino, Karcprzyk and Chiclana in [8] [10] [16].…”

Section: Introductionmentioning

confidence: 99%

Comparing distance to consensus of collective relations using OWA operators

Khalid

Awais

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Consensus is understood as a unanimous agreement by all experts in a group. The goal of consensus is not the selection of several options but to develop one decision that suits the interests of the entire group under consideration. In this paper, it is assumed that collective preferences are developed with the help of commonly used ordered weighted averaging operators but resultant relations do not exhibit any property of a consensus type. That is, consensus is not reached at the first attempt of ranking alternatives. Under such circumstances, the measure of distance to consensus can be successfully used to determine how far a group collectively is from consensus. The aim of this paper is to compare, where possible, the distance to consensus of collective relations compiled with the help of most commonly used ordered weighted averaging operators. The innovative part is that this measure helps in defining an upper and lower bound of distance to consensus of the resultant collective relations. With such an analysis at hand, experts can choose a suitable ordered weighting averaging operator to formulate a collective relation that exhibits a lower distance to consensus.

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Aggregation of fuzzy preference relations to multicriteria decision making

Cited by 25 publications

References 31 publications

Fuzzy Negotiation Solutions of n-Person Cooperative Games by Knowledge Engineering in Bargaining

Fuzzy Negotiation Solutions of n-Person Cooperative Games by Knowledge Engineering in Bargaining

Apply Cellular Wireless Location Technologies to Traffic Information Gathering

Comparing distance to consensus of collective relations using OWA operators

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