Stochastic cost efficiency evaluation of a supply chain

Abstract

The main goal of the paper is a consideration of cost efficiency evaluation models related to some supply chain when dealing with imprecise data. Data envelopment analysis (DEA) method is a non-parametric mathematical programming approach to assess the performance. This method is proposed for deterministic data and it can be generalized to inaccurate data, while considering real world applications. Here we consider data as random variables and after reviewing and introducing new models to evaluate cost efficiencies related to the special circumstances of the supply chain using DEA, these models are developed to probabilistic form. Also, deterministic and linear equivalents are proposed using the symmetric error structure of normal distributions. At final, by a numerical example, the proposed models are examined to show relationships of results.