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

Research output: Contribution to journalArticlepeer-review

2 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 languageEnglish
Pages (from-to)S21-S49
JournalRAIRO - Operations Research
Volume55
DOIs
Publication statusPublished - 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

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