Integrating Digital Twin and Optimization for First-Mile Milk Collection Systems: A Scenario-Based Evaluation Framework

This working paper develops a methodological approach for integrating mathematical optimization with a digital twin environment in the analysis of first-mile milk collection systems. The approach combines a mixed-integer linear programming (MILP) model for network design with a digital representation that enables the evaluation of system behavior under changing operating conditions. The optimization model determines the baseline configuration, including the location of collection points, capacity allocation, and producer assignments. This configuration is then embedded into the digital twin, where its performance is examined under a representative perturbation scenario involving a 20% reduction in milk supply. The analysis shows that the baseline configuration, while efficient under nominal conditions, is sensitive to variations in supply, leading to reduced utilization and higher unit costs. Allowing limited operational adjustments within the fixed network structure improves performance, although economic indicators do not fully return to baseline levels. The results also reveal uneven effects across performance dimensions, indicating the presence of trade-offs between economic, operational, and environmental outcomes. The contribution of this study lies in connecting optimization-based design with a digital evaluation environment that enables the assessment of network configurations beyond their initial formulation. The approach provides a structured way to examine how a given design responds to changing conditions without requiring immediate structural modifications. The analysis is illustrative and intended to demonstrate the integration mechanism. Future work will extend this approach through systematic scenario design, quantitative validation, and the incorporation of real-time data.