A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors

Outsourcing in logistics is a very significant theme and third-party reverse logistics (3PL) provider evaluation and selection has to be realized in a careful manner in order to provide the expected benefits. In this paper a new chance-constrained data envelopment analysis (CCDEA) approach is proposed to assist the decision makers to determine the most appropriate third-party reverse logistics (3PL) providers in the presence of both dual-role factors and stochastic data. A numerical example demonstrates the application of the proposed model.

► Stochastic data and dual-role factors are considered simultaneously.
► The proposed model does not demand exact weights from the decision maker.
► For the first time, the proposed model is used for the problem of 3PL provider selection.