Developing a new chance-constrained data envelopment analysis in the presence of stochastic data

Ehsan Momeni, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Outsourcing in logistics is a very significant theme and third-party reverse logistics (3PL) provider evaluation and selection has to be realised in a careful manner in order to provide the expected benefits. Data envelopment analysis (DEA) has been successfully used to select the most efficient supplier(s) in a supply chain. In this study, a new Russell chance-constrained data envelopment analysis (RCCDEA) approach is proposed to assist the decision-makers to determine the most appropriate 3PL providers in the presence of multiple performance measures that are uncertain. Because of the complexity of the proposed model, a genetic algorithm is presented as a solution procedure to obtain near to optimum solutions. The usefulness of the proposed model and algorithm was validated by its application to an illustrative example.

Original languageEnglish
Pages (from-to)169-194
Number of pages26
JournalInternational Journal of Business Excellence
Issue number3
Publication statusPublished - May 2012


  • 3PL providers selection
  • Chance-constrained data envelopment analysis
  • Data envelopment analysis
  • DEA
  • GA
  • Genetic algorithm
  • Russell measure
  • Russell model
  • Third-party reverse logistics provider

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management

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