Interval efficiency assessment using data envelopment analysis

  • Authors:

  • Ying-Ming Wang;Richard Greatbanks;Jian-Bo Yang

  • Affiliations:

  • Manchester Business School, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK and School of Public Administration, Fuzhou University, Fuzhou, Fujian 350002, PR China and Center for ...;Manchester Business School, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK;Manchester Business School, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK

  • Venue:
  • Fuzzy Sets and Systems
  • Year:
  • 2005

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Abstract

This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input-output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and @a-level sets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.