Loughborough University
Browse
Operations Research VP.pdf (239.41 kB)

Nonparametric production technologies with multiple component processes

Download (239.41 kB)
journal contribution
posted on 2017-08-18, 12:49 authored by Victor PodinovskiVictor Podinovski, Ole Bent Olesen, Claudia S. Sarrico
We 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 Research

Volume

66

Issue

1

Pages

282-300

Citation

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

2017-07-27

Publication date

2017-11-16

Notes

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.2017.1667

ISSN

0030-364X

eISSN

1526-5463

Language

  • en