A sustainable integrated model for multi-objective planning of an agri-food supply chain under uncertain parameters: A case study

The extended and complex nature of agri-food supply chain systems results in food loss and an asymmetrical flow of information. It is vital to integrate the cultivation, harvest, processing, and distribution decisions for designing a sustainable agri-food supply chain to minimize overall cost and create employment opportunities alongside considering global concerns. A multi-objective, integrated, sustainable mathematical model is presented in this study to maximize the revenue and employment generated while reducing the environmental impacts. The criteria for the evaluation of farmlands are derived from literature, and Geographical Information System (GIS) is used to obtain geospatial data to assess the performance of diverse farmlands across various criteria. The farmlands are then assessed and prioritized using the Best Worst Method (BWM). Among all criteria for selecting the farmlands, favorable temperature and land-use have the highest and lowest impact, respectively. Furthermore, a pricing model is proposed to estimate the price in various customer zones. The robust possibilistic model is suggested to take into account weather patterns, transportation costs, and customer zone demand under uncertain situation. The proposed model is illustrated in the Stevia processing plant in Iran and the tradeoffs between different model parameters and objective functions are studied, and the validity of the model is assessed by sensitive analyses. The outcomes show that to meet robustness, the number of active farmlands and warehouses should be increased by about 11%, which imposes a 10% cost on the model. Based on sensitive analysis, increases in production capacity and demand result in a significant rise in the profit function (12% and 16%, respectively), despite the fact that improvements to farmland and warehouse capacity have little effect on profit, indicating the need for managers to prioritize production rate and advertising. Moreover, the results show the best location for planting stevia, the optimum production rate, the proper number of warehouses, and their capacities in each period.