Application of Mixed-Integer Linear Programming Models for the Sustainable Management of Vine Pruning Residual Biomass: An Integrated Theoretical Approach

<i>Background</i>: This study explores the use of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a crucial resource for sustainable energy and material production. Efficient biomass logistics play a key role in supporting circular bioeconomy principles by improving resource utilization and reducing operational costs. <i>Methods</i>: Two optimization approaches are evaluated: a base MILP model designed for scenarios with single processing points and an advanced model that incorporates intermediate processing steps to enhance logistical efficiency. The models were tested using synthetic datasets simulating vineyard regions to assess their performance. <i>Results</i>: The models demonstrated significant improvements, achieving cost reductions of up to 30% while enhancing operational efficiency and resource utilization. The study highlights the scalability and real-world applicability of the proposed models. <i>Conclusions</i>: The findings underscore the potential of MILP models in optimizing biomass supply chains and advancing circular bioeconomy goals. However, key limitations, such as computational complexity and adaptability to dynamic environments, are noted. Future research should focus on real-time data integration, dynamic updates, and multi-objective optimization to improve model robustness and applicability across diverse supply chain scenarios.