A Linear Programming Relaxation DEA Model for Selecting a Single Efficient Unit with Variable RTS Technology

Authors

  • reza akhlaghi Department of Applied Mathematics, Islamic Azad University, Rasht, Iran
  • mohsen rostamy-malkhalifeh Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • alireza amirteimoori Department of Applied Mathematics, Islamic Azad University, Rasht, Iran
  • Sohrab Kordrostami Department of Applied Mathematics, Islamic Azad University, Lahijan, Iran

Abstract

The selection-based problem is a type of decision-making issue which involves opting for a single option among a set of available alternatives. In order to address the selection-based problem in data envelopment analysis (DEA), various integrated mixed binary linear programming (MBLP) models have been developed. Recently, an MBLP model has been proposed to select a unit in DEA with variable returns-to-scale technology. This paper suggests utilizing the linear programming relaxation model rather than the MBLP model. The MBLP model is proved here to be equivalent to its linear programming relaxation problem. To the best of the authors’ knowledge, this is the first linear programming model suggested for selecting a single efficient unit in DEA under the VRS (Variable Returns to Scale) assumption. Two theorems and a numerical example are provided to validate the proposed LP model from both theoretical and practical perspectives.

Downloads

Published

2021-12-22

Issue

Vol. 12 No. 2 (2021): CRORR

Section

CRORR Journal Regular Issue

License

- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal

- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).