## 1 Introduction
Thus, algorithms have been developed that allow approaching the problem of scheduling with different interruptions [22], algorithms that allow dealing with the machine's setup times [23], jobs with specific routes that avoid some steps [24; 25], multi-criteria algorithm methods [26; 27] and even algorithms that consider several sources of interruption simultaneously (job variation, job cancellation, job time variation, machine breakdown and new jobs) [28; 29; 30; 31; 32; 33]. Industry 4.0 and smart manufacturing [13] are also capable of creating a virtual copy of the physical world and make decentralized decisions [34], so new approaches can be seen based on decision-making schema [20] or adaptative scheduling [35]. Figure 1: Phases of a typical Steelmaking Continuous Casting process. However, despite these advances, it is common that a manual rescheduling takes place, minimizing the loss of time in the workshop [36]. The main reason for this manual rescheduling is the faster human reaction to unexpected events [37], based on their understanding and knowledge of past experiences. Furthermore, in real environments, the problems that arise are random and dynamic, so the parameters, limitations, and objectives change over time [38]. In general, the different contingencies in steelmaking production can be divided into three classes [39]. Firstly, there are contingencies that are resource related [40], such as the shortage of raw material, a lack of adequate personnel to carry out the necessary tasks or even machine breakdowns. Then, there are eventualities that are job related, such as the interruptions or cancelations of jobs, or changes in process or transport time. Finally, some contingencies are both resource and job related, such as a lack of energy or quality problems. One of the last problems to be solved in planning has to do with the performance of continuous casting machines and the optimization of the production process [41]. A common problem in modern steel mills is the loss of one of the continuous casting strands during casting, due to three main reasons: first, castability, deposition of material in the nozzles that prevents the passage of steel into the mold. Second, cold steel, causing the solidification of the steel itself, sealing the nozzle. Last, perforation/breakout of the liquid metal, which happens when the solid shell of the strand breaks and allows the still-molten metal contained within to spill out and foul the machine. These problems imply a loss of time during the continuous casting phase, and therefore a loss of productivity. In current planning, such losses are assumed as delays in the casting machine [42]. Scheduling in a steel plant is one of the most complicated problems of industrial organization. The steel mill is a living work environment where a multitude of unforeseen events that affect production can arise, whether they are operational efficiency, environmental parameters, job cancellations [42; 43], resource constrains [44] or others. In a simplified way, any contingency can be considered a trigger for a machine stop, but reality indicates that sometimes it simply affects the casting speed, or the closure of any of the lines. Some studies consider the casting time as a variable that allows optimizing the process time, avoiding sequence breaks. Most of the approaches do not contemplate these unforeseen events, assuming delays and generating new schedules based on the original parameters, due to the static characteristics of these schedules [45]. The scheduling problems have already been classified according to different criteria [46], including, mainly, two types of decisions [6]. * Batch decisions, in which the primary requirements of orders are transformed into production batches. There are two types of batches in steel mill production. One is called heat and the other is cast or tundish.