A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty

Selecting the most suitable optimal point among Pareto optimal points could help experts make an appropriate decision in an uncertain and complex situation. In this paper, an evaluation and ranking approach is proposed based on a hesitant fuzzy set environment to assess the Pareto optimal points obtained through the proposed biobjective multi-echelon supply chain model by locating distribution centers. In this respect, the proposed model has been utilized for perishable products based on fuzzy customers' demand. To address this issue, the possibilistic chance-constrained programming approach has been utilized based on the trapezoidal fuzzy membership function. Moreover, the proposed hesitant fuzzy ranking approach is constructed based on group decision analysis and the last aggregation approach. Thereby, the last aggregation approach by aggregating the experts' opinions in the last step could prevent the data loss. However, a case study about the perishable dairy products is considered to indicate the applicability of the proposed bi-objective multi-echelon supply chain model by locating distribution centers. Finally, a comparative analysis is provided between the obtained results and the current practice to show the feasibility and eciency of the proposed approach.