Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty

• A new model for supplier selection in a reverse logistics system is developed.
• The proposed model is solved by Monte Carlo simulation and fuzzy goal programming.
• The effectiveness of the modeling approach is demonstrated in a real case study.
• The performance of the solution method in obtaining trade-off solutions is analyzed.

In this research, we develop a fuzzy multi-objective mathematical model to identify and rank the candidate suppliers and find the optimal number of new and refurbished parts and final products in a reverse logistics network configuration. This modeling approach captures the inherent uncertainty in customers’ demand, suppliers’ capacity, and percentage of returned products as well as existence of conflicting objectives in reverse logistics systems. The objective functions in this study are defined as total profit, total defective parts, total late delivered parts, and economic risk factors associated with the candidate suppliers whereas the uncertainties are treated in a fuzzy environment. In order to avoid the subjective weighting from decision makers when solving the multi-objective model, a Monte Carlo simulation integrated with fuzzy goal programming is developed to determine the entire set of Pareto-optimal solutions of the proposed model. The effectiveness of the mathematical model and the proposed solution method in obtaining Pareto-optimal solutions is demonstrated in a numerical example from a real case study.