A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation

Madjid Tavana*, Hadi Shabanpour, Saeed Yousefi, Reza Farzipoor Saen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

The evaluation of sustainable suppliers is one of the most complex tasks in sustainable supply chain management (SSCM). Classical data envelopment analysis (DEA) and dynamic DEA (DDEA) models are heavily dependent on historical data and do not forecast future efficiencies of decision-making units (DMUs). The primary objective of this paper is to present a new predictive paradigm for ranking sustainable suppliers in SSCM. The proposed model combines goal programming and DDEA in an integrated and seamless paradigm to determine the future efficiencies of DMUs (suppliers). It also shifts the decision maker’s role from monitoring the past to planning the future. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

Original languageEnglish
Pages (from-to)3683-3696
Number of pages14
JournalNeural Computing and Applications
Volume28
Issue number12
DOIs
Publication statusPublished - Dec 1 2017

Keywords

  • Benchmarking
  • Decision-making units
  • Dynamic data envelopment analysis
  • Efficiency evaluation
  • Goal programming
  • Sustainable supplier selection

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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