## 2 Literature Review
[23] examined the kit, line-side, and sequencing methods with a total cost formulation that evaluates material preparation costs before assembly, picking, transportation from the central warehouse to the assembly line, and stocking. [PERSON] et al. [10] developed a cost model for supplying materials to assembly lines according to the kit, line-storage, and just-in-time production methods that depend on the part characteristics and performed sensitivity and parametric analyses to determine the best method for each material. [PERSON] et al. [18] extended the mixed-integer programming formulations in the literature by allowing flexible line-edge spacing by modeling different line feeding methods per material family for different material feeding methods. [PERSON] et al. [32] developed a mathematical model that aims to reduce the cost of a material feeding system. [PERSON] et al. [33], [PERSON] et al. [34], [PERSON] et al. [35], and [PERSON] et al. [36] investigated the potential of machine learning (ML) methodologies in addressing the material feeding problem. [PERSON] et al. [35] presented a DT model for the material feeding problem. [PERSON] et al. [37] presented a simulation-based model and statistical analysis for the material feeding strategy problem. As asserted by [PERSON] et al. [33], artificial neural network (ANN), decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (K-NN) are the most frequently applied ML models, and binary classification is the major dependent variable for production line problems. A literature review reveals significant advancements in understanding and modeling the problems associated with feeding materials during the assembly process. Many extant studies, beginning with [PERSON] and [PERSON] [2], adopt a binary or uniform approach, presuming a uniform feeding policy for all components. This simplification does not reflect the complexity of real-world assembly lines, where different components may require different material feeding strategies. In practice, many assembly lines are characterized by the deployment of multiple material feeding systems, which are utilized concurrently to accommodate various product types, production volumes, part characteristics, and supply chain constraints. This necessitates exploring hybrid material feeding systems capable of integrating and dynamically adjusting between different feeding methods. The implementation of such systems would enable manufacturers to optimize material handling processes in a flexible and adaptive manner, allowing for real-time adjustments based on shifting production needs and environmental conditions. Research on hybrid feeding systems, where multiple feeding strategies coexist and incorporate dynamic decision-making frameworks, ensuring that material feeding systems can be switched or combined in response to specific operational requirements, remains limited. Integrating traditional material feeding strategies with data, predictive analytics, and ML models can potentially optimize manufacturing systems' performance and cost efficiency. Recent research (e.g., [PERSON] et al. [33]; [PERSON] et al. [35]) introduces ML-based methodologies for material feeding problems, but applications remain exploratory and limited to specific ML models. Most models primarily aim to minimize costs, neglecting other crucial performance metrics that influence assembly lines' overall performance and effectiveness. In environments with high variability or frequent product changes, a system optimized only for the cost may lack flexibility and efficiency. Material feeding strategies prioritizing cost minimization can lead to quality issues or delays. The rapid pace of technological advancements in manufacturing, driven by Industry 4.0, increases the importance of system adaptability. Therefore, there is a need for multi-criteria decision-making models that address broader operational and strategic objectives beyond cost reduction.