TY - JOUR
T1 - Theory of binary-valued data envelopment analysis: an application in assessing the sustainability of suppliers.
T2 - an application in assessing the sustainability of suppliers
AU - Karimi, Balal
AU - Azadi, Majid
AU - Farzipoor Saen, Reza
AU - Fosso Wamba, Samuel
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022/2/3
Y1 - 2022/2/3
N2 - Purpose: The objective of this study is to present a binary-valued data envelopment analysis (DEA) theory. The authors’ proposed approach, for the first time, combines binary-valued and integer-valued theories concurrently in the DEA context. To do so, new production possibility sets (PPSs) with some distinguished features are developed. Design/methodology/approach: The authors address integer inputs and outputs in the proposed approach by introducing a new PPS. Findings: To take into account the binary data, the authors develop axiomatic DEA principles. The binary production principles guarantee any combination of convexity and feasibility. Furthermore, the authors develop a new DEA model to consider integer and real data. A case study is presented to show the usefulness of the developed models. Using the proposed models, the authors obtained benchmarks to solve the sustainable supplier selection problems. Originality/value: (1) For the first time, binary-valued and integer-valued theories are presented in an integrated DEA model. (2) To deal with the pure binary data, a new PPS is proposed. (3) To consider real, integer and binary data, a new PPS is introduced. (4) New technologies are developed to propose feasible solutions. (5) The proposed models can project inefficient decision-making units (DMUs) on efficiency frontier given binary, integer and real data. (6) A case study is given for the performance evaluation of sustainable suppliers.
AB - Purpose: The objective of this study is to present a binary-valued data envelopment analysis (DEA) theory. The authors’ proposed approach, for the first time, combines binary-valued and integer-valued theories concurrently in the DEA context. To do so, new production possibility sets (PPSs) with some distinguished features are developed. Design/methodology/approach: The authors address integer inputs and outputs in the proposed approach by introducing a new PPS. Findings: To take into account the binary data, the authors develop axiomatic DEA principles. The binary production principles guarantee any combination of convexity and feasibility. Furthermore, the authors develop a new DEA model to consider integer and real data. A case study is presented to show the usefulness of the developed models. Using the proposed models, the authors obtained benchmarks to solve the sustainable supplier selection problems. Originality/value: (1) For the first time, binary-valued and integer-valued theories are presented in an integrated DEA model. (2) To deal with the pure binary data, a new PPS is proposed. (3) To consider real, integer and binary data, a new PPS is introduced. (4) New technologies are developed to propose feasible solutions. (5) The proposed models can project inefficient decision-making units (DMUs) on efficiency frontier given binary, integer and real data. (6) A case study is given for the performance evaluation of sustainable suppliers.
KW - Binary-valued data
KW - Data envelopment analysis (DEA)
KW - Efficiency measurement
KW - Integer-valued data
KW - Sustainable suppliers
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UR - http://www.scopus.com/inward/citedby.url?scp=85123996323&partnerID=8YFLogxK
U2 - 10.1108/imds-09-2021-0555
DO - 10.1108/imds-09-2021-0555
M3 - Article
AN - SCOPUS:85123996323
SN - 0263-5577
VL - 122
SP - 682
EP - 701
JO - Industrial Management and Data Systems
JF - Industrial Management and Data Systems
IS - 3
M1 - 3
ER -