## 2 Working Hypothesis--Problem Identification  
During day-to-day industrial processes, various problems may arise, such as machine malfunctions, human errors, damaged materials and external failures. These issues can cause delays in manufacturing operations and disrupt established planning, resulting in multiple problems at the business level, such as delayed delivery dates, increased costs and unmet demand. Real-time scheduling is essential to deal with these types of issues. It allows problems to be dealt with promptly, either before, during, or immediately after they occur. Any delay in decision making results in economic cost and reduced quality and efficiency, which industries must try to limit in order to maximise benefits. To deal with production problems that may arise during production, real-time scheduling can use alternative work centres. When doing so, it is important to consider the unique characteristics of each work centre, as they may have different capacities despite being alternatives. For example, in a scenario where a manufacturing lathe produces machine tools and has both expert and apprentice workers, there are likely to be significant differences in the resulting parts. These differences can be categorised as speed (process time), quality (features) and cost (labour and energy costs). Production planning is usually conducted under ideal or standard conditions; that is, operations are planned assuming that both the expert resource and the apprentice will work under normal circumstances and will produce parts within a given time, cost and quality range, according to the expected average. However, in real time, multiple problems can arise, such as the expert having a bad day, being sick, having an accident, or encountering issues with the materials used. When these incidents arise, during real-time planning, the objectives being pursued must be clear at all times in order to act quickly. If the time, cost and quality variables are evaluated numerically, different decisions could have varying consequences for each of these variables. For instance, if the expert is unavailable, the apprentice could be assigned to complete a certain task, but this may result in lower-quality work taking longer to complete, although it could be less expensive. This decision has several implications, including the potential loss of customers, cost savings, potential financial losses, if repairs are necessary, and delays. This problem becomes significantly more complex when there are multiple work resources, materials, machinery and tools, as well as different AMR models of varying capacities. Additionally, external factors can also impact more complex production processes. To enable real-time planning in these more complex production environments, current technologies provide tools for numerical evaluation of these variables in real time and the necessary changes and adaptations in these environments. To achieve this, Industry 4.0 technologies are utilised, including the digital transformation of production through cyber-physical systems. These industrial control systems use software capable of utilising the potential of different technologies, such as the IoT (Internet of Things), RDFI (radiofrequency), robotics, and machine learning, to achieve greater automation, convergence of the physical and digital worlds, delegation of certain decisions to machines, data retrieval, and increased product customisation. Industry 4.0 [1] is a convergence of existing and emerging technologies that interact with each other, creating a dynamic and constantly evolving concept. Therefore, Industry 4.0 encompasses various technologies including cloud computing, big data, artificial intelligence, data analysis, edge computing, mobiles, data network technologies, ICSs (industrial control systems) (HMI--human-machine interface; SCADA--industrial automation), business control systems (MESs--manufacture execution systems; ERP--enterprise resource planning), sensors and actuators, MEMSs (microelectomechanycal systems) and transducers and AMRs.