## 3 Analysis of Literature Resources
### Production Planning, Control, and Scheduling
PPC's main operations (e.g., job scheduling, performance optimization, and dynamic control) are strongly supported by several I4.0 technological pillars [19; 20], such as cyber-physical systems (CPSs), cyber-physical production systems (CPPSs), sensors, big data analytics (BDA), the Industrial Internet of Things (IIoT), and cloud computing [19; 21]. For instance, PPC uses Information and Communication Technologies (ICTs) by the horizontal and vertical integration of some manufacturing technologies into the software and tools of production scheduling processes [22]. The capabilities explored for BDA based on AI include real-time optimization integrated into sales and operations planning (S&OP) processes, enterprise resource planning (ERP), and material requirements planning (MRP) to predict material flows and shop floor operations behavior [19], and lastly master production scheduling (MPS) to enhance forecasting, scheduling, and decision-making along the supply chain [19; 23]. In the I4.0 context, PPC--together with the management of maintenance, quality, and supply chain processes--considers functions determining the production plan, such as the global quantities to be produced in order to satisfy production objectives in terms of profitability and productivity, allowing the real-time synchronization of resources as well as product mass-customization [20]. The overall objective of PPC is the creation of reliable production plans whose deployment is facilitated by the increased computing power of AI solutions [23]. Prediction-based solutions outperform traditional planning methods--whether based on static historical data or real-time resource measurements--by providing a global, batch-level perspective. Designed for large-scale manufacturing systems, they leverage cloud infrastructure to handle high-volume data streams, enabling real-time optimization, scalability, and fault tolerance [24]. In order to respect production constraints as much as possible, these approaches need to determine production items (sequence, time, and machine) and accomplish the request for each of them within a time horizon [16; 25]. Moreover, production planning often intersects with complex challenges such as uncertain capacities, fluctuating demand, hybrid flow shops, load balancing, and the integration of scheduling in dynamic industrial settings [16]. Indeed, in the production planning field, scheduling problems generally refer to short-term planning at the operational level, particularly focusing on coordinating resources use by planning processing times, sequencing operations, and ultimately optimizing overall production time and resource utilization [25]. Sometimes various unforeseen disturbances (e.g., machine breakdowns, worker illnesses, procurement risks) may occur during production planning, which may cause inconsistencies between planning and reality. Therefore, mathematical formulations and algorithms are needed to solve timely sequencing in production problems in order to avoid the occurrence of production accidents and unexpected deviations representing bottlenecks along the production system [25]. For this reason, scheduling problems can be defined as Resource-Constrained Project Scheduling Problems (RCPSPs), aiming to find optimal plans with minimal makespan for each activity based on precedence relationships, while respecting resource availability restrictions [26]. In order to achieve the scheduling objective, it is necessary to consider constraint conditions and scheduling results as first-level information in production operations, while raw material supply and real-time equipment data are considered as second-level information. However, production environments are dynamic, often influenced by disturbances such as machine failures or supply chain disruptions.