Metaheuristic algorithms for a sustainable saffron supply chain network considering government policies and product quality under uncertainty

Iranian saffron products hold a unique position in the global market as the most highly valued agricultural and medicinal commodities. The various uses of saffron make it clear that there is a need for special attention to the supply chain network. Unfortunately, the absence of an integrated supply chain network within the saffron industry has resulted in significant challenges related to supply management and demand fulfillment. Addressing real-world uncertainties is paramount when developing models for optimization problems. Therefore, this research proposes a multi-objective optimization model for designing a saffron supply chain network under uncertainty. The model objectives are to decrease the total cost of the supply chain, increase job opportunities and economic development in regions, and improve the quality of products. The proposed mathematical model is solved using the interactive fuzzy method to deal with multiple functions. Furthermore, possibilistic chance constrained programming is employed to effectively manage uncertain variables such as demand, cost, and social parameters within the model. To demonstrate the applicability and validity of the proposed model and solution method, a real case study was conducted in Khorasan Razavi province, Iran. Additionally, because of the complexity of the proposed model in large-scale networks, NSGA-II and MOSA algorithms are proposed. Different parameters are analyzed to determine their impact on the results so that decision-makers can choose values more accurately. The sensitivity analysis and statistical tests performed on the results support the performance of the proposed model. Overall, the results demonstrate that the exact method and metaheuristic algorithms are capable of solving the problem in different dimensions. The computational results derived from this model offer invaluable managerial insights, empowering decision-makers to align their strategies and preferences more effectively.