# Robotic Cell Layout Optimization Using a Genetic Algorithm
## 4 Discussion  
The optimization of the robotic cell's area using the genetic algorithm led to a more compact and efficient design. The algorithm was able to identify station layouts that reduced the required space without compromising the robot's mobility or the cell's functionality, which is especially beneficial in manufacturing environments with space constraints. However, the algorithm does not account for spaces needed for operator movement, so it is recommended to consider these factors when designing a robotic cell. A limitation of the use of the algorithm is that it doesn't consider the possibility of the rotation of the workstations as a possible solution. This approach of no rotation is very useful for the algorithm, as, with the intersections, it is more difficult to count with rotation workstations, which only a few researchers have performed, like [PERSON] and [PERSON] [29]. Some further research its need it to evaluate if rotation of workstations needs to be considered.  
Optimizing the travel times and the area of the cell can result in lower operational costs, as both energy consumption and robot wear due to unnecessary movements were reduced. This translates into increased equipment lifespan and greater efficiency in resource utilization. This algorithm of this work only considers as a valid solution that the location of the workstation is within the reach of the robot, while other works considered the Denavit-Hartenberg equations [30] of a proposed robot; this gives an advantage, since the solution is evaluated as reachable or not from the same algorithm. For future work, we will seek to introduce the D-H equations of various robot models so that the user has the ability to evaluate their solutions without having to simulate them.  
\begin{table}
\begin{tabular}{c c c} \hline
**Trajectory** & **Current Transfer Time (s)** & **Algorithm Transfer Time (s)** \\ \hline
1 to 3 & 2.78 & 2.40 \\
2 to 3 & 2.61 & 2.38 \\
4 to 3 & 2.90 & 2.10 \\
5 to 3 & 2.41 & 2.84 \\ Total time & 10.70 & 9.72 \\ \hline \end{tabular}
\end{table}
Table 7: Trajectory time, assembly cell.  
\begin{table}
\begin{tabular}{c c c} \hline
**Robotic Cell** & **Current Transfer Time (s)** & **Algorithm Transfer Time (s)** \\ \hline Screwing & 11.65 & 10.25 \\ Machining & 15.61 & 14.55 \\ Assembly & 10.70 & 9.72 \\ Total time & 37.96 & 34.52 \\ \hline \end{tabular}
\end{table}
Table 8: Trajectory time, production line.  
Regarding the limitation of the algorithm using only one robot per robotic cell, this significantly reduces the complexity of the problem but also limits the optimization potential. In more advanced industrial environments, where multiple robots can work simultaneously in the same cell or in adjacent cells, the interactions between the robots offer opportunities to improve efficiency and reduce cycle times. However, optimizing such systems with multiple robots introduces a new level of complexity, as it is necessary to manage the coordination between robots to avoid collisions and minimize downtime.  
The implementation of the genetic algorithm can enhance the overall productivity of the robotic cell. By minimizing travel times and maximizing space utilization, the number of production cycles completed within a given period increased, which benefits the overall production capacity. This means that it is not necessary to continue performing the traditional approach as the only way to design and determine the location of the workstations, which now can be assisted with an algorithm to receive some location proposals for the workstations. Also, it is important to evaluate the use of some other evolutionary algorithms, as, according to some research, [PERSON] [11] found that Particle Swarm Optimization could lead to better results in robotic cell optimization.