## 6 Conclusions  
This paper presented a novel MILP model designed to optimize the selection, allocation, and utilization of different quality inspection methodologies in multi-product assembly systems. The main contribution lies in its ability to simultaneously select the most appropriate inspection methodologies and determine optimal sampling rates, while minimizing total quality-related costs. Compared to existing approaches, the proposed model uniquely integrates heterogeneous inspection methods, explicitly accounts for detection errors, and supports sampling rate optimization in a multi-product environment. According to the literature review (Table 1), no previous contribution simultaneously addresses all of these aspects within a unified optimization framework. The model was validated through computational experiments using synthetically generated data and a real-world case study from the automotive sector. The synthetic data allowed for an in-depth analysis of the model's scalability, revealing that the number of quality characteristics and inspection stations are key drivers of computational complexity. The real-world case study demonstrated the practical applicability of the model, highlighting the benefits of integrating machine vision systems and flexible sampling strategies into quality control planning. The results show that machine vision technologies provide a significant performance advantage, reducing total inspection-related costs by over 40% when compared to scenarios where they are not available. However, the current model formulation presents some limitations. The current model formulation does not capture the potential improvement in defect detection capabilities over time, recognizing that human operators may enhance their performance as they gain experience. Future extensions could model this learning effect by introducing detection probabilities that progressively improve based on the number of inspections performed by operators, thereby capturing experience accumulation. It is noted that incorporating such learning dynamics would likely introduce nonlinearities into the model, potentially requiring the adoption of heuristic or metaheuristic solution methods to reduce computational complexity. In terms of misclassification errors during inspection, false negatives are already modeled and contribute to penalty costs associated with non-compliance. In contrast, false positives, where compliant units are incorrectly classified as defective, are not modeled. The false positives could lead to unnecessary rework operations and additional resource consumption. Future model enhancements could incorporate both types of misclassification errors to further increase the realism and practical relevance of the inspection planning. Another limitation concerns the empirical validation based on a single real-world case study with relatively limited volume data. Extensive testing was conducted using synthetically generated datasets covering a wide range of problem configurations to mitigate this. Beyond the automotive sector, the proposed model is applicable to a wide range of discrete manufacturing industries such as electronics, aerospace, and medical device production, where diverse inspection technologies and multi-product lines are prevalent. These sectors similarly face challenges in balancing inspection cost, accuracy, and operational constraints, making the model broadly relevant. Future research could thus focus on applying the model to a broader set of industrial scenarios to further validate its robustness and generalizability. Furthermore, integrating the model with real-time production monitoring systems (e.g., IoT-enabled inspection data), adapting it for remanufacturing settings, where inspection requirements vary by product condition, or extending it to support dynamic rescheduling in response to process disruptions or quality alerts are interesting avenues worth exploring as potential directions. Conceptualization, M.R., C.C., M.G. ([PERSON]), A.R., and M.G. ([PERSON]); data curation, C.C.