TY - JOUR
T1 - Recommending investment opportunities given congestion by adaptive network data envelopment analysis model
T2 - Assessing sustainability of supply chains
AU - Hajaji, Hossein
AU - Yousefi, Sara
AU - Farzipoor Saen, Reza
AU - Hassanzadeh, Amir
N1 - Publisher Copyright:
© EDP Sciences, ROADEF, SMAI 2021.
PY - 2021
Y1 - 2021
N2 - Nowadays, forward-thinking companies move beyond conventional structures of organizations and consider all parties of the supply chain. The objective of this paper is to present an adaptive network data envelopment analysis (DEA) model to evaluate overall and divisional efficiency of sustainable supply chains in the presence of desirable and undesirable outputs. Our adaptive network DEA model can assess overall and divisional efficiency of supply chains given managerial and natural disposability. Also, it suggests new investment opportunity given congestion type. A case study is presented.
AB - Nowadays, forward-thinking companies move beyond conventional structures of organizations and consider all parties of the supply chain. The objective of this paper is to present an adaptive network data envelopment analysis (DEA) model to evaluate overall and divisional efficiency of sustainable supply chains in the presence of desirable and undesirable outputs. Our adaptive network DEA model can assess overall and divisional efficiency of supply chains given managerial and natural disposability. Also, it suggests new investment opportunity given congestion type. A case study is presented.
KW - Congestion
KW - Network data envelopment analysis (NDEA)
KW - Range-adjusted measure (RAM)
KW - Sustainable investment
KW - Sustainable supply chain management (SSCM)
KW - Undesirable outputs
UR - http://www.scopus.com/inward/record.url?scp=85103785046&partnerID=8YFLogxK
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U2 - 10.1051/ro/2019059
DO - 10.1051/ro/2019059
M3 - Article
AN - SCOPUS:85103785046
SN - 0399-0559
VL - 55
SP - S21-S49
JO - RAIRO - Operations Research
JF - RAIRO - Operations Research
ER -