Abstract
This chapter presents some of the recently developed analytical methods for studying the sensitivity of DEA results to variations in the data. The focus is on the stability of classification of DMUs (decision making units) into efficient and inefficient performers. Early work on this topic concentrated on developing algorithms for conducting such analyses after it was noted that standard approaches for conducting sensitivity analyses in linear programming could not be used in DEA. However, recent work has bypassed the need for such algorithms. It has also evolved from the early work that was confined to studying data variations in one input or output for one DMU. The newer methods described in this chapter make it possible to analyze the sensitivity of results when all data are varied simultaneously for all DMUs.
Part of the material in this chapter is adapted from article in the Journal of Productivity Analysis, Cooper WW, Li S, Seiford LM, Tone K, Thrall RM, Zhu J, Sensitivity and stability analysis in DEA: some recent developments, 2001, 15, 217–46, and is published with permission from Kluwer Academic Publishers.
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Cooper, W.W., Li, S., Seiford, L.M., Zhu, J. (2011). Sensitivity Analysis in DEA. In: Cooper, W., Seiford, L., Zhu, J. (eds) Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 164. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6151-8_3
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