## 7 Discussion
### Limitations and Future Work  
This work, by considering specific characteristics and calculations related to the hydraulic and physicochemical properties of wine, can be computationally intensive and require a substantial amount of detailed information. This complexity may limit the model's applicability in environments with limited data resources or where exhaustive information collection is not feasible. Therefore, it is advisable to explore new approaches based on machine learning to estimate hydraulic performance and processing time using historical process information, which could simplify calculations and enhance the model's adaptability to different operational conditions. Additionally, other models that incorporate probabilistic scenarios can be investigated, which would contribute to a more comprehensive consideration of processing time within the production plan, allowing for better management of the inherent uncertainty in vicitcultural processes. Another aspect to consider is the development of a new weekly planning method that optimizes resource allocation and batch sequencing over longer periods. Finally, the use of heuristics and metaheuristics as resolution methods could offer more scalable and computationally efficient solutions, especially in larger wineries with a wide variety of SKUs and multiple production lines. These future research directions would enhance operational efficiency and also increase the flexibility and robustness of optimization models in the wine sector. Given the inherent variability in the physicochemical properties of wine and their impact on filtration performance, it is essential to develop mathematical optimization models under uncertainty to rigorously determine the optimal filtration-system configuration. These models could integrate variables such as projected demand, expected fouling frequency, average hydraulic properties, and the availability of technical resources, with the aim of identifying both the optimal batch-to-line assignment matrix and the total filtration area required. By incorporating uncertain scenarios and stochastic parameters, such models would enable the evaluation of different infrastructure and operational combinations, identifying those that achieve a balance between equipment investment and operational costs related to maintenance, downtime, and production efficiency. This approach would support strategic decision making aimed at optimizing system profitability in the medium and long term, offering a robust tool for capacity planning in complex production environments such as the wine industry. Furthermore, we acknowledge that the current model estimates processing times externally based on predefined physicochemical parameters. While effective, this approach limits the exploration of how variations in critical parameters (such as viscosity, filter permeability, and clogging rate) impact not only processing time but also the sequence-dependent setup tasks required. As these three variables interact and jointly affect the filtration performance of a batch, conducting a structured sensitivity analysis would provide valuable insights into how this variability propagates through the scheduling model and influences its outputs. Although any variation in these parameters represents a different wine profile, such an analysis remains essential for understanding how to better manage operational risk within a production plan. In particular, it could support the classification and handling of high operational risk batches that require special scheduling strategies. Additionally, studying this interaction can inform future efforts to incorporate processing time calculations directly within the optimization model, especially considering heuristics-based methods, where dynamic parameter-driven modeling could enhance adaptability without overwhelming computational complexity. In response to the computational challenges commonly faced by smaller-scale wineries, it is necessary to develop simplified and adaptable versions of the proposed model.