Improving the discrimination power and weights dispersion in the data envelopment analysis

BAL H., ÖRKCÜ H. H., Celebioglu S.

COMPUTERS & OPERATIONS RESEARCH, cilt.37, sa.1, ss.99-107, 2010 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 1
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.cor.2009.03.028
  • Dergi Adı: COMPUTERS & OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.99-107
  • Anahtar Kelimeler: Data envelopment analysis, Coal programming, Discrimination power, Unit invariance, RELATIVE EFFICIENCY, DEA MODELS, RESTRICTIONS, RANKING, UNITS
  • Gazi Üniversitesi Adresli: Evet

Özet

Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Beside of its popularity, DEA has some drawbacks such as unrealistic input-output weights and lack of discrimination among efficient DMUs. In this study, two new models based on a multi-criteria data envelopment analysis (MCDEA) are developed to moderate the homogeneity of weights distribution by using goal programming (GP). These goal programming data envelopment analysis models, GPDEA-CCR and GPDEA-BCC, also improve the discrimination power of DEA. (C) 2009 Elsevier Ltd. All rights reserved.