Rights statement: This is the peer reviewed version of the following article: Ehrgott, M, Hasannasab, M, Raith, A. A multiobjective optimization approach to compute the efficient frontier in data envelopment analysis. J Multi‐Crit Decis Anal. 2019; 26: 187– 198. https://doi.org/10.1002/mcda.1684 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/mcda.1684 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 28/08/2019 |
|---|---|
| <mark>Journal</mark> | Journal of Multi-Criteria Decision Analysis |
| Issue number | 3-4 |
| Volume | 26 |
| Number of pages | 12 |
| Pages (from-to) | 187-198 |
| Publication Status | Published |
| <mark>Original language</mark> | English |
Data envelopment analysis is a linear programming-based operations research technique for performance measurement of decision-making units. In this paper, we investigate data envelopment analysis from a multiobjective point of view to compute both the efficient extreme points and the efficient facets of the technology set simultaneously. We introduce a dual multiobjective linear programming formulation of data envelopment analysis in terms of input and output prices and propose a procedure based on objective space algorithms for multiobjective linear programmes to compute the efficient frontier. We show that using our algorithm, the efficient extreme points and facets of the technology set can be computed without solving any optimization problems. We conduct computational experiments to demonstrate that the algorithm can compute the efficient frontier within seconds to a few minutes of computation time for real-world data envelopment analysis instances. For large-scale artificial data sets, our algorithm is faster than computing the efficiency scores of all decision-making units via linear programming.