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Nonparametric production technologies with multiple component processes
journal contribution
posted on 2017-08-18, 12:49 authored by Victor PodinovskiVictor Podinovski, Ole Bent Olesen, Claudia S. SarricoWe develop a nonparametric methodology for assessing the efficiency of decision making units operating in a production technology with several component processes. The latter is modeled by the new multiple hybrid
returns-to-scale (MHRS) technology, formally derived from an explicitly stated set of production axioms. In contrast with the existing models of data envelopment analysis (DEA), the MHRS technology allows the incorporation of component-specific and shared inputs and outputs that represent several proportional (scalable) component production processes, as well as nonproportional inputs and outputs. Our approach does not require information about the allocation of shared inputs and outputs to component processes
or any assumptions about this allocation. We demonstrate the usefulness of the suggested approach in an application in the context of secondary education, and also in a Monte Carlo study based on a simulated data generating process.
History
School
- Business and Economics
Department
- Business
Published in
Operations ResearchVolume
66Issue
1Pages
282-300Citation
PODINOVSKI, V.V., OLESEN, O.B. and SARRICO, C.S., 2018. Nonparametric production technologies with multiple component processes. Operations Research, 66(1), pp. 282-300.Publisher
© (Institute for Operations Research and Management Sciences (INFORMS)Version
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-07-27Publication date
2017-11-16Notes
This paper was accepted for publication in the journal Operations Research and the definitive published version is available at https://doi.org/10.1287/opre.2017.1667ISSN
0030-364XeISSN
1526-5463Publisher version
Language
- en