## 3 Analysis of Literature Resources
### Production Planning, Control, and Scheduling
As a result, production scheduling must consider the following:Complexity: Scheduling must handle multiple factors such as the timing of each production operation (including arrival, start-up, loading, unloading, processing, waiting between machines, and transport times) as well as external factors like the availability of material and human resources, such as skilled operators. With the increasing complexity of the scheduling problems, the computational effort as well as the time required to find an optimal solution--or a sub-optimal one--rises [25]. * Randomness: Dynamic environments and conditions (e.g., machine breakdowns, order changes, etc.) affect stability [25]. * Multiple constraints: Sequence-dependent setup times, varying process types (e.g., single jobs vs. batch processing), and fluctuating process times complicate scheduling and operations [25; 27]. Indeed, relevant uncertainties such as production delays, plant shutdowns, rush orders, price fluctuations, and shifts in demand can distort previously optimal or sub-optimal schedules [28]. Single-machine scheduling often overlooks maintenance, assuming constant system availability, an approach that raises the risk of breakdowns and disrupts optimal production performance [29]. * Multi-objective requirements: Balancing productivity, cost-efficiency, and customer satisfaction under competing Key Performance Indicators (KPIs) [25; 30; 31; 32]. This involves minimizing losses and inventory costs while maximizing output and efficiency. However, achieving these goals is challenging due to their interconnected nature and operational constraints [25]. A core challenge in production scheduling lies in reconciling conflicting priorities; e.g., customers demand lower costs, higher efficiency, and better service, while manufacturers aim to simplify production and maximize profit [3]. Therefore, finite capacities and the emergence of Work-In-Process effects add to this complexity, which is often underestimated by traditional ERP tools due to high human dependency [3; 27; 33]. Hence, integrating real-time data through CPPS and AI-driven systems is essential for predictive, flexible, and adaptive PPC [24]. Therefore, the data-driven integration of various PPC modules, e.g., MPS, MRP, Capacity Requirements Planning, which work in a synchronic manner is essential for predictive capacity planning and the real-time traceability of machine and product status [19]. Production scheduling and planning directly impact on business performance in terms of efficient scheduling which means that both plant maintenance and resource availability are met within their required timeframes, ensuring a high level of quality in production control [29]. Production scheduling problems, particularly Job Shop Scheduling Problems (JSSPs) and Flexible JSSPs (FJSSPs), are classified as NP-hard due to their combinatorial complexity [18; 34; 80]. Traditional heuristics and rule-based systems can approximate solutions but often fall short in accuracy, scalability, and adaptability. Since exact solutions cannot be computed in polynomial time, heuristics and rule-based methods are typically used to obtain approximate solutions. However, these approaches have some drawbacks, as achieving greater accuracy often demands high computational effort and is time consuming, while relying on general rules may lead to oversimplifications [18]. Thus, the implementation of Advanced Planning and Scheduling (APS) systems enhanced by Artificial Intelligence (AI)--especially Machine Learning (ML) and metaheuristics--is strongly recommended. In the next sections a particular subset of AI, Machine Learning (ML) [26] and its related techniques, together with metaheuristic techniques will be discussed regarding their application in manufacturing production contexts to solve challenging combinatorial optimization problems. These approaches offer dynamic optimization, reduced manual intervention, and improved responsiveness in high-variability manufacturing contexts.