Abstract

This study explores the application of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a critical resource for sustainable energy and material production. Two optimization approaches are evaluated, as follows: a base MILP model designed for scenarios with single processing points and an advanced model incorporating intermediate processing steps to enhance logistical efficiency. Using synthetic datasets that simulate vineyard regions, the models demonstrate potential cost reductions of up to 30%, showcasing significant improvements in operational efficiency and resource utilization. This study underscores the scalability and real-world feasibility of the proposed models, highlighting their alignment with circular bioeconomy principles. Additionally, it addresses key limitations such as computational complexity and adaptability to dynamic environments. Future research directions are outlined, focusing on real-time data integration, dynamic updates, and multi-objective optimization to further enhance model robustness and applicability in diverse supply chain scenarios.

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