Positive weights in data envelopment analysis

Maryam Hasannasab, Dimitris Margaritis (), Israfil Roshdi and Paul Rouse
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Maryam Hasannasab: The University of Auckland
Dimitris Margaritis: The University of Auckland
Israfil Roshdi: Islamic Azad University
Paul Rouse: The University of Auckland

Journal of Productivity Analysis, 2024, vol. 62, issue 3, No 7, 363 pages

Abstract: Abstract Data Envelopment Analysis (DEA) models give each decision making unit freedom to assign values to the input-output weights maximising its efficiency score. However, there is no guarantee that the optimal weights obtained from a DEA model are always positive which poses a problem, in both theory and practice. This paper offers new linear programming models to deal with the zero weights problem without restricting the weights or placing prior value judgments on them while maintaining the original DEA frontier. These models generate a profile of weights with the maximum number of positive weights applicable to both envelopment and multiplier DEA settings. These linear programs still allow weight flexibility and are independent of the solver. We show how these models can be modified to provide unit-specific positive weights. We illustrate the relevance of our approach using artificial and real world data.

Keywords: Linear Programming; Data envelopment analysis; Positive weights; Efficiency measurement; C61 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11123-023-00682-3

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