A robust fuzzy possibilistic programming for a new network GP-DEA model to evaluate sustainable supply chains

This paper proposes a hybrid goal programming-data envelopment analysis (GP-DEA) model in a network structure to present improvement solutions and rank units of a supply chain. The improvement solutions are presented for all efficient and inefficient units based on experts' requirements. Therefore, the goals are considered as fuzzy values. To deal with uncertainty, a suitable fuzzy possibilistic approach is employed. Given robust optimization approach, the units of the supply chain are ranked based on penalties of deviations from goals as feasibility robustness and also the average and standard deviation of deviations as optimality robustness. One advantage of the proposed model is that it can determine balancing values and improvement solutions for flows among the units of a supply chain which have dual-roles factors so that the deviations from their goals are minimized. The proposed robust network GP-DEA model is run in a case study. The outcome of this paper can be used to evaluate and rank all types of supply chains with different network structures.