A joint chance-constrained data envelopment analysis model with random output data
Rashed Khanjani Shiraz (),
Madjid Tavana () and
Hirofumi Fukuyama ()
Additional contact information
Rashed Khanjani Shiraz: University of Tabriz
Madjid Tavana: La Salle University
Hirofumi Fukuyama: Fukuoka University
Operational Research, 2021, vol. 21, issue 2, No 19, 1255-1277
Abstract Data envelopment analysis (DEA) is a mathematical programming approach for evaluating the technical efficiency performances of a set of comparable decision-making units that transform multiple inputs into multiple outputs. The conventional DEA models are based on crisp input and output data, but real-world problems often involve random output data. The main purpose of the paper is to propose a joint chance-constrained DEA model for analyzing a real-world situation characterized by random outputs and crisp inputs. After developing the model, we carry out the following: First, we obtain an upper bound of this stochastic non-linear model deterministically by applying a piecewise linear approximation algorithm based on second-order cone programming; Second, we obtain a lower bound with use of a piecewise tangent approximation algorithm, which is also based on second-order cone programming; and then we use a numerical example to demonstrate the applicability of the proposed joint chance-constrained DEA framework.
Keywords: Data envelopment analysis; Joint chance-constrained programming; Random data; Second-order cone programming (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s12351-019-00478-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:21:y:2021:i:2:d:10.1007_s12351-019-00478-0
Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351
Access Statistics for this article
Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis
More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().