Monte Carlo-based instance generator for agri-food supply chain operations assessment

Addressing sustainability challenges within the Agri-Food system is pivotal to meeting global food demand without endangering the resilience of ecological systems. However, the complexity of Agri-Food Supply Chain (AFSC) operations and the scarcity and heterogeneity of primary data present significant obstacles to effective management. To tackle such issues, this paper illustrates a novel Agri-food supply chain Monte Carlo-based Istance GeneratOr tool (AMIGO). This tool exploits aggregated demand data and geospatial information on supply chain nodes to generate order release scheduling and realistic distribution pathways according to logistical feasibility. AMIGO design and implementation are outlined, highlighting its key components and operational principles. A case study is presented as a validation testbed. This tool is applied to generate two scenario instances for horticultural products within AFSCs. By varying supply chain features, settings, and configurations, this tool captures the nuanced dynamics of different supply chain configurations. Enabling empirical analysis, AMIGO effectively provides valuable insights into supply chain management and decision-making. Overall, it aids agri-food industry stakeholders, supports policy-making efforts and fosters innovation in AFSC management.