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A Multiobjective Programming Method for Ranking All Units Based on Compensatory DEA Model

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  • Haifang Cheng
  • Yishi Zhang
  • Jianhu Cai
  • Weilai Huang

Abstract

In order to rank all decision making units (DMUs) on the same basis, this paper proposes a multiobjective programming (MOP) model based on a compensatory data envelopment analysis (DEA) model to derive a common set of weights that can be used for the full ranking of all DMUs. We first revisit a compensatory DEA model for ranking all units, point out the existing problem for solving the model, and present an improved algorithm for which an approximate global optimal solution of the model can be obtained by solving a sequence of linear programming. Then, we applied the key idea of the compensatory DEA model to develop the MOP model in which the objectives are to simultaneously maximize all common weights under constraints that the sum of efficiency values of all DMUs is equal to unity and the sum of all common weights is also equal to unity. In order to solve the MOP model, we transform it into a single objective programming (SOP) model using a fuzzy programming method and solve the SOP model using the proposed approximation algorithm. To illustrate the ranking method using the proposed method, two numerical examples are solved.

Suggested Citation

  • Haifang Cheng & Yishi Zhang & Jianhu Cai & Weilai Huang, 2014. "A Multiobjective Programming Method for Ranking All Units Based on Compensatory DEA Model," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, November.
    • Handle: RePEc:hin:jnlmpe:807843
    DOI: 10.1155/2014/807843
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