Assessing sustainability of supply chains by fuzzy Malmquist network data envelopment analysis: Incorporating double frontier and common set of weights

Amirali Fathi, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Sustainable supply chain management (SSCM) is one of the most significant topics. Today, assessing sustainability and choosing the best supply chain is an important goal for companies. One of the techniques that can be used for evaluating the sustainability of supply chains is data envelopment analysis (DEA). This study contributes to the knowledge of sustainability assessment by (i) proposing a double frontier network DEA (NDEA) model with common set of weights (CSW), (ii) dealing with fuzzy data to assess sustainability, and (iii) deriving a CSW model for Malmquist productivity index (MPI). The CSW models consider the same set of weights for all decision making units (DMUs) so that all DMUs get the highest sustainability score. The fuzzy MPI (FMPI) is used to assess the sustainability of supply chains with the trapezoidal fuzzy number. The FMPI reflects the productivity change over time. The proposed model can assess the sustainability of supply chains and takes into account different confidence levels in two periods and can fully rank DMUs. Our model can measure the sustainability of supply chains via fuzzy MPI. Using our proposed approach, the decision-makers can determine the optimistic, pessimistic, and double frontier sustainability of supply chains. To prove the applicability of the proposed model, the sustainability of customs centers is assessed.

Original languageEnglish
Article number107923
JournalApplied Soft Computing
Volume113
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Common set of weights (CSW)
  • Double frontiers
  • Fuzzy Malmquist productivity index (FMPI)
  • Network data envelopment analysis (NDEA)
  • Sustainability assessment

ASJC Scopus subject areas

  • Software

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