Efficiency ranking with common set of weights based on data envelopment analysis and satisfaction degree

Traditional DEA models allow individual decision-making units (DMUs) to arbitrarily determine the weights of all variables to calculate their efficiencies. This flexibility in selecting the weights is a great advantage for many DMUs, but it also brings some defects such as non-uniqueness of weights sets and the appearance of zero-value weights. Different sets of weights to calculate efficiencies violate the requirement that DMUs should be compared on the same base, leading to the efficiency ranking being not accepted by decision-makers. In this paper, we provide a DEA-based approach for obtaining DMUs' efficiencies, which assumes that DMUs are collective rationality and its objective is to maximise the satisfaction degrees of all the DMUs. Then, we provide a maxmin model and two corresponding algorithms for generating the common set of weights (CSW). Lastly, the proposed approach is applied to the ranking of 17 forest districts and is compared with other methods.

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