On measuring the inefficiency with the inner-product norm in data envelopment analysis

On measuring the inefficiency with the inner-product norm in data envelopment analysis

0.00 Avg rating0 Votes
Article ID: iaor20022052
Country: Netherlands
Volume: 133
Issue: 2
Start Page Number: 377
End Page Number: 393
Publication Date: Sep 2001
Journal: European Journal of Operational Research
Authors: ,
Keywords: programming: convex
Abstract:

A technique for assessing the sensitivity of efficiency classifications in Data Envelopment Analysis (DEA) is presented. It extends the technique proposed by Charnes et al. An organization's input–output vector serves as the center for a cell within which the organization's classification remains unchanged under perturbations of the data. The maximal radius among such cells can be interpreted as a stability measure of the classification. Our approach adopts the inner-product norm for the radius, while the previous work does the polyhedral norms. For an efficient organization, the maximal-radius problem is a convex program. On the other hand, for an inefficient organization, it is reduced to a nonconvex program whose feasible region is the complement of a convex polyhedral set. We show that the latter nonconvex problem can be transformed into a linear reverse convex program. Our formulations and algorithms are valid not only in the CCR model but in its variants.

Reviews

Required fields are marked *. Your email address will not be published.