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The SAPUVETNET Projects: experiences of intersectoral collaboration and research/training in Veterinary Public Health across Latin America and Europe
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D. De Meneghi, E.Bert, PC. Porporato, D.Pattono, N.Cediel, M. Vilhena, L. Padre, S. Arroube, P. Baltasar, A.Custodio; L.C. Villamil, J.Romero, C.Mutis; I.Sommerfelt, C.Lopez. - 2011
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Group decision making with incomplete fuzzy linguistic preference relations
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In: http://sci2s.ugr.es/publications/ficheros/2009-alonso-IJIS.pdf (2009)
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Computing with words in decision making: foundations, trends and prospects
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In: http://sci2s.ugr.es/publications/ficheros/2009-FODM-Herrera-CWW.pdf (2009)
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Abstract Decision Support
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In: http://decsai.ugr.es/~viedma/papers/2007-Herrera-Viedma-EJOR-IOWA.pdf (2006)
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A Consistency Based Procedure to Estimate Missing Pairwise Preference Values
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In: http://decsai.ugr.es/~viedma/papers/cbp07.pdf (2004)
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Basis for a Consensus Model in Group Decision Making with Linguistic Preferences
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In: http://decsai.ugr.es/~viedma/papers/gdmi07.pdf (1995)
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Agents and Ambient Intelligence: Case Studies
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In: http://www.softcomputing.net/jaihc.pdf
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An Evaluation Methodology of Quality for Health Web Sites based on Fuzzy Linguistic Modelling
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In: http://www.gimac.uma.es/ipmu08/proceedings/papers/145-Moreno.pdf
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Applying Linguistic OWA Operators in Consensus Models under Unbalanced Linguistic Information
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In: http://sci2s.ugr.es/publications/ficheros/Springer-265-167-186.pdf
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A CONSENSUS MODEL FOR GROUP DECISION MAKING IN UNBALANCED FUZZY LINGUISTIC CONTEXTS
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In: http://sci2s.ugr.es/publications/ficheros/ESTYLF%202008-449-456.pdf
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A Consistency-Based Procedure to Estimate Missing Pairwise Preference Values
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In: http://sci2s.ugr.es/publications/ficheros/IJIS2008.pdf
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Improving the User-System Interaction in a Web Multi-agent System Using Fuzzy Multi-granular Linguistic Information
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In: http://sci2s.ugr.es/publications/ficheros/C09.pdf
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A Multi-granular Linguistic Hierarchical Model To Evaluate The Quality Of Web site Services
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In: http://sci2s.ugr.es/publications/ficheros/LinguisticHierarchichal-2006.pdf
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A consensus model for group decision making problems with unbalanced fuzzy linguistic information
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In: http://sci2s.ugr.es/publications/ficheros/2009-cabrerizo-ITDM.pdf
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A MOBILE LINGUISTIC DECISION SUPPORT SYSTEM TO HELP USERS IN THEIR E-COMMERCE ACTIVITIES
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In: http://sci2s.ugr.es/publications/ficheros/estylf2010nacho.pdf
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A Decision Aid System to Provide Consistent Linguistic Preference Relations
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In: http://sci2s.ugr.es/publications/ficheros/C10.pdf
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A Consistency Based Procedure to Estimate Missing Pairwise Preference Values
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In: http://www.cse.dmu.ac.uk/%7Echiclana/publications/IJIS2006.pdf
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A CONSENSUS MODEL FOR GROUP DECISION MAKING PROBLEMS WITH UNBALANCED FUZZY LINGUISTIC INFORMATION
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A CONSENSUS MODEL FOR GROUP DECISION MAKING PROBLEMS WITH UNBALANCED FUZZY LINGUISTIC INFORMATION
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Abstract:
Most group decision making (GDM) problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts' opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e. using term sets that are not uniformly and symmetrically distributed. The aim of this paper is to present a consensus model for GDM problems with unbalanced fuzzy linguistic information. This consensus model is based on both a fuzzy linguistic methodology to deal with unbalanced linguistic term sets and two consensus criteria, consensus degrees, and proximity measures. To do so, we use a new fuzzy linguistic methodology improving another approach to manage unbalanced fuzzy linguistic information,1(Int. J. Intell. Syst.22(11) (2007) 1197–1214), which uses the linguistic 2-tuple model as representation base of unbalanced fuzzy linguistic information. In addition, the consensus model presents a feedback mechanism to help experts for reaching the highest degree of consensus possible. There are two main advantages provided by this consensus model. First, its ability to cope with GDM problems with unbalanced fuzzy linguistic information overcoming the problem of finding different discrimination levels in linguistic term sets. Second, it supports the consensus process automatically, avoiding the possible subjectivity that the moderator can introduce in this phase. ; Fuzzy linguistic modeling, unbalanced linguistic term set, group decision making, consensus
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URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622009003296
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