Welcome to the InfoSci Platform
Reference Hub2
A Unified Mathematical Model for Stochastic Data Envelopment Analysis

A Unified Mathematical Model for Stochastic Data Envelopment Analysis

Basma E. El-Demerdash, Assem A. Tharwat, Ihab A. A. El-Khodary
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 15
ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781799860907|DOI: 10.4018/IJSSMET.2021010108
Cite Article Cite Article

MLA

El-Demerdash, Basma E., et al. "A Unified Mathematical Model for Stochastic Data Envelopment Analysis." IJSSMET vol.12, no.1 2021: pp.127-141. http://doi.org/10.4018/IJSSMET.2021010108

APA

El-Demerdash, B. E., Tharwat, A. A., & El-Khodary, I. A. (2021). A Unified Mathematical Model for Stochastic Data Envelopment Analysis. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 12(1), 127-141. http://doi.org/10.4018/IJSSMET.2021010108

Chicago

El-Demerdash, Basma E., Assem A. Tharwat, and Ihab A. A. El-Khodary. "A Unified Mathematical Model for Stochastic Data Envelopment Analysis," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 12, no.1: 127-141. http://doi.org/10.4018/IJSSMET.2021010108

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Efficiency measurement is one aspect of organizational performance that managers are usually interested in determining. Data envelopment analysis (DEA) is a powerful quantitative tool that provides a means to obtain useful information about the efficiency and performance of organizations and all sorts of functionally similar, relatively autonomous operating units. DEA models are either with a constant rate of return (CRS) or variable return to scale (VRS). Furthermore, the models could be input-oriented or output-oriented. In many real-life applications, observations are usually random in nature; as a result, DEA efficiency measurement may be sensitive to such variations. The purpose of this study was to develop a unified stochastic DEA model that handles different natures of variables independently (random and deterministic) and can be adapted to model both input/output-oriented problems, whether it is CRS or VRS. The chance-constrained approach was adopted to handle the stochastic variables that exist in the model. The developed model is implemented through an illustrative example.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.