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A GDM Method Based on Granular Computing for Academic Library Management

  • Francisco Javier Cabrerizo
  • Raquel Ureña
  • Juan Antonio Morente-Molinera
  • Enrique Herrera-Viedma
Part of the Studies in Big Data book series (SBD, volume 10)

Abstract

An academic library, as a service organization, has to maintain a level of service quality that, satisfying its users, will assure funding for its existence and development. To do so, the general manager, which is in charge of distributing the funding, asks to the staff of the library about their opinions in the allocation of the budget. An important issue here is the level of agreement achieved among the staff before making a decision. In this paper,we propose a group decision making method based on granular computing aiding to the general manager to decide about funding distribution according to the staff’s opinions. Assuming fuzzy preference relations to represent the preferences of the staff, a concept of a granular fuzzy preference relation is developed, where each pairwise comparison is formed as an information granule instead of a single numeric value. It offers the required flexibility to increase the level of agreement within the staff, using the fact that the most suitable numeric representative of the fuzzy preference relation is selected.

Keywords

Group decision making Academic library Granular computing Consensus 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Francisco Javier Cabrerizo
    • 1
  • Raquel Ureña
    • 2
  • Juan Antonio Morente-Molinera
    • 2
  • Enrique Herrera-Viedma
    • 2
  1. 1.Department of Software Engineering and Computer SystemsUniversidad Nacional de Educación a Distancia (UNED)MadridSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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