## 3 Results
### Evaluation Criteria  
After obtaining the optimal solution by applying the algorithm, a direct comparison was made for each of the 500 cases with the solution obtained by applying the original scheduling method, after a standard rescheduling is carried out. As a result, a difference was obtained between the time to standard production (TSP) obtained from the algorithm and the TSP obtained in the plant in a conventional way, called TSPbase. This difference is called the time improvement index (TII) and is given in minutes. The improvement time is always considered between standard correction of the scheduling carried out in the plant, and the rescheduling proposed by the algorithm. The results of the time improvement index for the billet machine grouped by steel class and by number of active lines after the disruptive event (L) are shown in Table 3. Additionally, the number of simulations or tests carried out for each course is shown. All the solutions granted by the algorithm have a positive TII, meaning this that every solution, at least, is as good as the standard one. This is easily checked, if the standard solution is one of the 13 considered by the algorithm in every situation. When TII is 0, the algorithm has chosen the decision that fits with the standard one, producing no improvement in these cases, but also no losses. Results of the TII for the bloom machine are shown on Table 4, grouped by steel class and number of active lines after T. This machine, as defined, has fewer lines, and it treats a lower quantity of steel classes, as it can be seen. Comparing the data in Tables 3 and 4, an average time saving is obtained until reaching the standard production rate again on the billet machine of 28.13 min, and on the bloom machine of 22.11. The total time-saving average, obtained by multiplying the partial means by the number of partial tests, adding them up and dividing by the total number of tests, is 26.41 min, which represents a notable increase in production if the decision made by the algorithm is adopted. In addition, there are notable differences in the results obtained, mainly depending on the number of lines lost. Table 5 shows the TII as a function of the number of active lines (L) after the line loss event, considering all the tests of the billet machine, without differentiating steel classes. The most alike results with traditional scheduling are those in which the uncertainty is fewer. For example, L = 0 means a complete shutdown of the machine, where the following castings could be only rearranged or reapplied, in addition to forcing the return of the N heat. This reduction of the possible paths to take in the decision tree is translated into a  
greater match when it comes to finding the optimal solution, rearm or reapply. Something similar occurs when L = 1, because by considerably increasing the casting time, there is a risk that the steel will stay cold in the tundish, causing premature solidification and the closure of that last line. Due to this, the usual solution is to completely close the machine, avoiding continuing the sequence casting the rest of the tundish by a single line. On the opposite side, the loss of a single line (L = 5), due to the high casting speed of the billet machine compared to other stations in the plant, hardly influences the rest of the process, so the solution from the original scheduling (continue casting), it is hardly improved by the algorithm. When the number of lines after the disruptive event (L) is three or four, or even two in some cases, the algorithm develops its greatest potential, mainly because the uncertainties are greater. Regarding the analysis of the bloom machine, it can be seen in Table 4 that the width difference between steel grade formats in the assigned steel grades is translated into a significant variation in the results obtained. This is because while the algorithm works