Recommending investment opportunities given congestion by adaptive network data envelopment analysis model: Assessing sustainability of supply chains

Hossein Hajaji; Sara Yousefi; Reza Farzipoor Saen; Amir Hassanzadeh

doi:10.1051/ro/2019059

Recommending investment opportunities given congestion by adaptive network data envelopment analysis model: Assessing sustainability of supply chains

Hossein Hajaji, Sara Yousefi, Reza Farzipoor Saen^*, Amir Hassanzadeh

^*Corresponding author for this work

Operations Management & Business Statistics

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	S21-S49
Journal	RAIRO - Operations Research
Volume	55
DOIs	https://doi.org/10.1051/ro/2019059
Publication status	Published - 2021

Keywords

Congestion
Network data envelopment analysis (NDEA)
Range-adjusted measure (RAM)
Sustainable investment
Sustainable supply chain management (SSCM)
Undesirable outputs

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science Applications
Management Science and Operations Research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1051/ro/2019059

Cite this

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title = "Recommending investment opportunities given congestion by adaptive network data envelopment analysis model: Assessing sustainability of supply chains",

abstract = "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.",

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author = "Hossein Hajaji and Sara Yousefi and {Farzipoor Saen}, Reza and Amir Hassanzadeh",

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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

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DO - 10.1051/ro/2019059

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VL - 55

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JO - RAIRO - Operations Research

JF - RAIRO - Operations Research

ER -

Recommending investment opportunities given congestion by adaptive network data envelopment analysis model: Assessing sustainability of supply chains

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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