Sustainability assessment of supply chains by a novel robust two-stage network DEA model: a case study in the transport industry

Amirali Fathi, Balal Karimi, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The transport industry is one of the main contributors to environmental pollution. Assessing the efficiency of this industry helps organizations to enhance their awareness of performance and develop managerial strategies. The need for sustainable development in the transportation industry requires efficiency analysis. Data envelopment analysis (DEA) is a popular approach for efficiency evaluation. Traditional DEA assumes that the dataset should be crisp. However, in real-world problems, there might be imprecise data. On the other hand, the network DEA (NDEA) measures the efficiency of multistage processes. The NDEA models deal with the internal structure of decision-making units (DMUs). This study offers a novel analysis and contribution to the knowledge of efficiency evaluation by (1) proposing a novel robust two-stage NDEA (RTNDEA) model to evaluate the sustainability of supply chains and (2) dealing with undesirable outputs and uncertain conditions for efficiency analysis. The proposed model can fully rank DMUs. To demonstrate the applicability of the proposed approach, the sustainability of intercity passenger transportation is assessed.

Original languageEnglish
Pages (from-to)6101-6118
Number of pages18
JournalSoft Computing
Issue number13
Publication statusPublished - Jul 2022


  • Robust optimization
  • Robust two-stage network data envelopment analysis (RTNDEA)
  • Sustainable supply chain management (SSCM)
  • Transport industry
  • Undesirable outputs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology

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