A multi-echelon globalized agro-industrial supply chain under conditions of uncertainty: A two-stage fuzzy-possibilistic mixed-integer linear programming model

Supply chain (SC) design problem is probably one of the most critical challenges in the production/operations management field, with lasting effects on customer service and cost measurements. Managers are therefore obliged to find the best possible SC configuration that provide the highest level of responsiveness and operational efficiency, which subsequently improves customer satisfaction and fosters loyalty. In that vein, this research proposes a general approach to determine a resilient strategic design–i.e., able to rapidly adjust to market changes and disruptions, offering a competitive edge in an increasingly globalized economy–of a singular multi-layered and multi-product globalized agro-industrial SC with direct shipping in conditions of uncertainty. In order to obtain the shortest distance to move and store products from the supplier echelon–or crops–to the last echelon in the SC–or customers–, a two-stage fuzzy-possibilistic mixed-integer linear programming (FP-MILP) model is formulated and numerically evaluated through different restrictive conditions. The proposed FP-MILP model–based on the blending-problem approach–incorporates deterministic and fuzzy datasets in the objective function and constraints, efficiency calculations in crops and feedstocks, as well as different thermal floors and cultivation geographic areas. To illustrate the application of the FP-MILP model, several experiment scenarios are performed on a real four-tier globalized agro-industrial SC, and the results obtained are analyzed and compared with each other.