TY - JOUR
T1 - Assessing sustainability of supply chains by chance-constrained two-stage DEA model in the presence of undesirable factors
AU - Izadikhah, Mohammad
AU - Saen, Reza Farzipoor
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/12
Y1 - 2018/12
N2 - Sustainable supply chain is recognized as a key component of corporate responsibility. Despite conventional data envelopment analysis (DEA) models that view decision making units (DMUs) as black boxes, two-stage DEA models take into account intermediate measures within a DMU. However, there might be stochastic data. Objective of this paper is to present a new stochastic two-stage DEA model in the presence of undesirable data. We present some linear models that obtain lower and upper bounds of efficiencies of stages 1 and 2. Also, we propose a linear model that calculates overall efficiency of DMUs. Meanwhile, we extend our proposed model for dealing with stochastic data in the presence of undesirable data. A case study demonstrates applicability of our approach.
AB - Sustainable supply chain is recognized as a key component of corporate responsibility. Despite conventional data envelopment analysis (DEA) models that view decision making units (DMUs) as black boxes, two-stage DEA models take into account intermediate measures within a DMU. However, there might be stochastic data. Objective of this paper is to present a new stochastic two-stage DEA model in the presence of undesirable data. We present some linear models that obtain lower and upper bounds of efficiencies of stages 1 and 2. Also, we propose a linear model that calculates overall efficiency of DMUs. Meanwhile, we extend our proposed model for dealing with stochastic data in the presence of undesirable data. A case study demonstrates applicability of our approach.
KW - Chance-constrained data envelopment analysis (DEA)
KW - Efficiency
KW - Intermediate products
KW - Stochastic data
KW - Sustainability of supply chain
KW - Two-stage DEA model
KW - Undesirable data
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U2 - 10.1016/j.cor.2017.10.002
DO - 10.1016/j.cor.2017.10.002
M3 - Article
AN - SCOPUS:85031126994
SN - 0305-0548
VL - 100
SP - 343
EP - 367
JO - Computers and Operations Research
JF - Computers and Operations Research
ER -