A new non-radial directional distance model for data envelopment analysis problems with negative and flexible measures

Madjid Tavana*, Mohammad Izadikhah, Mehdi Toloo, Razieh Roostaee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Data envelopment analysis (DEA) is a mathematical approach for evaluating the efficiency of decision-making units that convert multiple inputs into multiple outputs. Traditional DEA models measure technical (radial) efficiencies by assuming the input and output status of each performance measure is known, and the data associated with the performance measures are non-negative. These assumptions are restrictive and limit the applications of DEA to real-world problems. We propose a new extended non-radial directional distance model, which is a variant of the weighted additive model, to cope with negative data. We then extend our model and use flexible measures, which play the role of both inputs and outputs, to cope with the unknown status of the performance measures. We also present a case study in the automotive industry to exhibit the efficacy of the models proposed in this study.

Original languageEnglish
Article number102355
JournalOmega (United Kingdom)
Publication statusPublished - Jul 2021


  • Automotive industry
  • Data envelopment analysis
  • Directional distance function
  • Flexible measures
  • Negative data
  • Productivity
  • Supplier selection

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management

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