TY - JOUR
T1 - Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains
AU - Izadikhah, Mohammad
AU - Azadi, Majid
AU - Shokri Kahi, Vahid
AU - Farzipoor Saen, Reza
N1 - Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Data envelopment analysis (DEA) is a method for measuring performance of decision making units (DMUs). Conventional DEA models view DMUs as black boxes. Network DEA (NDEA) models have been developed to overcome this shortfall. This paper develops a new NDEA model based on modified enhanced Russell measure model. This paper measures performance of humanitarian supply chains (HSCs) by an NDEA model. Capabilities of the proposed model are addressed by theorems. However, in the real world, there might be stochastic data. This paper presents a stochastic version of the proposed NDEA model to measure the performance of HSCs. We analyse main properties of our model. We present a case study to demonstrate the applicability of the proposed model.
AB - Data envelopment analysis (DEA) is a method for measuring performance of decision making units (DMUs). Conventional DEA models view DMUs as black boxes. Network DEA (NDEA) models have been developed to overcome this shortfall. This paper develops a new NDEA model based on modified enhanced Russell measure model. This paper measures performance of humanitarian supply chains (HSCs) by an NDEA model. Capabilities of the proposed model are addressed by theorems. However, in the real world, there might be stochastic data. This paper presents a stochastic version of the proposed NDEA model to measure the performance of HSCs. We analyse main properties of our model. We present a case study to demonstrate the applicability of the proposed model.
KW - data envelopment analysis (DEA)
KW - humanitarian disasters
KW - humanitarian supply chain
KW - network data envelopment analysis (NDEA)
KW - performance measurement
KW - stochastic network DEA
UR - http://www.scopus.com/inward/record.url?scp=85049608510&partnerID=8YFLogxK
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U2 - 10.1080/00207543.2018.1480840
DO - 10.1080/00207543.2018.1480840
M3 - Article
AN - SCOPUS:85049608510
SN - 0020-7543
VL - 57
SP - 662
EP - 682
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 3
ER -