A hybrid decision-making method using robust programming and interval-valued fuzzy sets for sustainable-resilient supply chain network design considering circular economy and technology levels

Abstract

This study introduces a new three-stage optimization approach with a circular economy perspective for sustainable-resilient supply chain network design for perishable products. The proposed model specifies the number of facilities and the number of products flowing within the supply chain in case of disruption. The three contributions of the mathematical model are considering product lifetime, monitoring financial resources, and selecting technology levels. It incorporates suppliers’ green image and circular economy rating in the supply chain network structure. Epistemic uncertainty is considered in the optimization model to deal with the unknown capacity, cost, and demand. The study includes three main stages. In the first stage, to determine suppliers' green image and circular economy rating, a new interval-valued fuzzy (IVF)-compromise decision-making method is presented based on possibilistic mean and standard deviation evaluations. In the second stage a new multi-objective mathematical model is proposed to reduce total costs, reduce environmental impacts, and increase social sustainability. To deal with uncertainties in the mathematical model, a new IVF-robust solution approach is introduced. In the third stage, the solution is incorporated with the AUGMECON2 method to produce separate Pareto-optimal solutions, offering a hybrid of trade-offs between cost, emissions, and social responsibility. A case study in the food, dairy, and drink industry is presented to show how the suggested approach might be applied. Finally, several sensitivity analyses and comprehensive comparisons of the proposed approach with the literature were performed.