A multi-granular linguistic model for management decision-making in performance appraisal

Andrés, Rocío de; García-Lapresta, José Luis; Martínez, Luis

doi:10.1007/s00500-008-0387-8

Cited by 65 publications

(29 citation statements)

References 31 publications

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“…Thus, for decision making, the use of linguistic labels makes expert judgment more reliable and informative and are more appropriate for approximating the preference values too complex to be represented using precise numerical values (Herrera and Herrera-Viedma 2000;Herrera et al 2008;Marimin et al 1998;Marimin et al 2002;Zadeh 1975). Fuzzy linguistic approaches for decision making have been applied to various fields (de Andrés et al 2010;Lu et al 2008;Halliwell and Shen 2009;Herrera et al 2009;Moreno et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Consensus reaching models of linguistic preference relations based on distance functions

2011

Soft Comput

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In group decision making (GDM) using linguistic preference relations to obtain the maximum degree of agreement, it is desirable to develop a consensus process prior to the selection process. This paper proposes two consensus models with linguistic information to support the GDM consensus reaching process. Two different distance functions between linguistic preference relations are introduced to measure both individual consistency and group consensus. Based on these measures, the consensus reaching models are developed. The two models presented have the same concept that the expert whose preference is farthest from the group preference needs to update their opinion according to the group preference relation. In addition, the convergence of the models is proved. After achieving the predefined consensus level, each expert's consistency indexes are still acceptable under the condition that the initial preference relations are of satisfactory consistency. Finally, an example is given to show the effectiveness of the models and to verify the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Consensus reaching models of linguistic preference relations based on distance functions

2011

Soft Comput

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“…Qualitative reasoning techniques, specifically order-ofmagnitude models, are considered an appropriate mathematical framework to represent expert opinions or preferences through a hierarchical model with linguistic labels (Andrés et al 2010;Soto 2011;Herrera et al 2008).…”

Section: Appendix A: the Qualitative Absolute Order-of-magnitude Modelmentioning

confidence: 99%

A measure of perceived performance to assess resource allocation

et al. 2015

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Performance measurement is a key issue when a company is designing new strategies to improve resource allocation. This paper offers a new methodology inspired by classic importance-performance analysis (IPA) that provides a global index of importance versus performance for firms. This index compares two rankings of the same set of features regarding importance and performance, taking into account underperforming features. The marginal contribution of each feature to the proposed global index defines a set of iso-curves that represents an improvement in the IPA diagram. The defined index, together with the new version of the diagram, will enable the assessment of a firm's overall performance and, therefore, enhance decision making in the allocation of resources. The proposed methodology has been applied to a Taiwanese multi-format retailer and managerial Communicated by V. Loia.

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“…Consequently, the development of adequate tools to manage and model multi-granular linguistic information becomes very important in the resolution of this type of GDM problem [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts

Mata

Pérez

Zhou

et al. 2014

Knowledge-Based Systems

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A crucial step in group decision making (GDM) processes is the aggregation of individual opinions with the aim of achieving a "fair" representation of each individual within the group. In multi-granular linguistic contexts where linguistic term sets with common domain but different granularity and/or semantic are used, the methodology widely applied until now requires, prior to the aggregation step, the application of a unification process. The reason for this unification process is the lack of appropriate aggregation operators for directly aggregating uncertain information represented by means of fuzzy sets. With the recent development of the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate fuzzy sets, alternative approaches to multi-granular linguistic GDM problems are possible. Unlike consensus models based on unification processes, this paper presents a new T1OWA based consensus methodology that can directly manage linguistic term sets with different cardinality and/or semantic without the need to perform any transformation to unify the information. Furthermore, the linguistic information could be assumed to be balanced or unbalanced in its mathematical representation, and therefore the new T1OWA approach to consensus is more general in its application than previous consensus reaching processes with muti-granular linguistic information. To test the goodness of the new consensus reaching approach, a comparative study between the T1OWA based consensus model and the unification based consensus model is carried out using six randomly generated GDM problems with balanced multi-granular information. When distance between fuzzy sets used in the T1OWA based approach is defined as the corresponding distance between their centroids, a higher final level of consensus is achieved in four out of the six cases although no significant differences were found between both consensus approaches.

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A multi-granular linguistic model for management decision-making in performance appraisal

Cited by 65 publications

References 31 publications

Consensus reaching models of linguistic preference relations based on distance functions

Consensus reaching models of linguistic preference relations based on distance functions

A measure of perceived performance to assess resource allocation

Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts

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