Multi-echelon agri-food supply chain network design integrating operational and strategic objectives: a case of public distribution system in India

The global concern to ensure the availability of food for the growing world's population draws urgent attention towards the inefficiencies in agri-food supply chains. Agri-food supply chains are inherently complex to manage than other supply chains mainly because of their multi-echelon structure, deteriorating product quality with time and changes in storage conditions which leads to significant amount of food loss and wastage. Additionally, any natural or man-made disaster further disrupts the chain and leads to high food loss, high supply chain costs, reduced food availability and poor food quality. Hence, there is a need to design resilient and efficient agri-food supply chain network for optimal multi-echelon storage and distribution to reduce food loss and quality degradation. For this purpose, a Fuzzy Multi Objective Linear Program (FMOLP) is proposed in this paper for integrated food procurement, storage and distribution under cost, resilience and quality considerations. The proposed model integrates the short-term operational objective of cost optimization with the long-term sustainable objectives of food loss minimization and resilience maximization. The proposed FMOLP is illustrated using a realistic case of Public Distribution System using the data benchmarked with the numbers reported by the Food corporation of India. The detailed computational analysis carried out in the paper in investigates three categories of problem sizes to compare and contrast the decisions using different strategies and to provide organizational, operational and policy insights on the trade-off between cost, food loss and resilience.

Automated summary

The complexity of agri-food supply chains leads to food loss and waste, and to address this issue, an FMOLP model is proposed to minimize food loss and enhance the resilience of the food supply chain. Through experimental analysis and case simulation, insights are provided on the trade-off between cost, food loss, and resilience.