## 4 Discussion  
The improvements shown in the previous tables demonstrate the validity of the designed algorithm, so it can be applied as a valid approach to the problem of continuous casting rescheduling, effectively combining batch and scheduling decisions in the same algorithm from the point of view of a situation that implies loose of lines in the caster and improving productivity. Some other approximations based on expertise tree decision may be carried, focusing on quality problems along the metallurgy phase, considering not only the productivity but the economic impact of not achieving the desired quality.  
Most of the algorithms developed for improving the effectivity of a rescheduling in the continuous casting machine process are focused on obtained a quick response from the system, gaining agility when reprogramming all the tasks in the different stations [36], but none of them try to take advantage of reprogramming the batch decisions previously taken, changing only the MPS. Because of this, the algorithm proposed is a suitably tool for any steel plan, no matter which scheduling algorithm is used.  
As for the main advantages of the algorithm, apart from the results showing the capacity of production increase, two of them must be highlighted: first, the algorithm is scalable and adaptable, being suitable for any steel plant, whichever machine distribution it has, only needing to redefine the tree decisions; and second, the algorithm has been designed to be used as a tool to be used at the same time as the main scheduling algorithm and only as a consulting tool, so the program could be installed easily in the steel plant,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Bloom Casting Machine**} \\ \hline
**Steel Grade** & **L** & **TII (min)** & **Qty. Test** \\ \hline  & 0 & 2.74 & 22 \\ BG125 & 1 & 15.62 & 22 \\  & 2 & 34.60 & 34 \\  & Total & 20.26 & 78 \\ \hline  & 0 & 6.63 & 22 \\ BP235 & 1 & 8.47 & 22 \\  & 2 & 61.30 & 20 \\  & Total & 24.35 & 64 \\ \hline Bloom casting machine & Total & 22.11 & 142 \\ \hline \hline \end{tabular}
\end{table}
Table 4: TII in bloom casting machine, grouped according to steel classes and active lines.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{4}{c}{**Billet Casting Machine**} \\ \hline
**L** & **TII (min)** & **Qty. Test** \\ \hline
0 & 2.37 & 66 \\
1 & 7.12 & 54 \\
2 & 26.71 & 64 \\
3 & 52.82 & 71 \\
4 & 63.27 & 58 \\
5 & 8.86 & 45 \\ Total & 28.13 & 358 \\ \hline \hline \end{tabular}
\end{table}
Table 5: TII in billet machine, grouped according to active lines.  
ensuring that no interference with the main program may occur. These two conditions make the algorithm suitable to be installed, adapted and used in any steel plant.  
One of the main disadvantages of the proposed algorithm is that the changes proposed to improve production may affect the entire supply chain from the steel plant to the subsequent rolling system. This inconvenience can be avoided by increasing restrictions on the tree algorithm, as it is fully scalable and adaptable, in order to secure minimum requisites of production defined by the supply chain department. The more restrictions stated in the decisions tree, the less effective the algorithm will be, as the difference between the standard solution adopted in the plant will be closer to the one proposed.  
To improve the effectiveness of the program, without the needed of increase the restrictions, future studies should focus on the relationship between the tree decision of the steel plant, and the batch decisions of the rolling plants.