# Robotic Cell Layout Optimization Using a Genetic Algorithm
## 1 Introduction
In [9], design criteria for a robotic cell are defined, design candidates are represented by a sequence pair scheme to avoid interference between components of the assembly system, and the use of dummy components is proposed to represent design areas where components are scarce. Objective functions are formulated, and optimal solutions are obtained using a genetic algorithm. Numerical evaluations are performed to illustrate the effectiveness of the proposed method. The existence of a relationship between design area and manipulability is concluded. That is, too small a design area reduces manipulability, since the robot arm must undergo more radical movements during assembly operations. In [10], the location of the work centers and the robot are parameterized as the homogeneous transformation matrix with respect to the environment. Each component is assigned a cylindrical envelope, and the interference is modeled with the circular projection. The robot joints are also projected to resolve collisions in 2D space. The deepest collision point is removed outside the cylindrical envelope along the vertical or radial direction. And the trajectory is segmentally replanned according to the relocated point. The optimization is achieved in a cascade flow, and the heuristic algorithm is applied. Experiments performed on a robotic ultrasonic shot peening work cell validate the effectiveness of the proposed method. No improvements in process times are reported. In [11], five nature-inspired algorithms, a genetic algorithm, differential evolution, an artificial bee colony, a charge search system, and particle swarm optimization are proposed. Design area criteria, operation time, and robot manipulability are simultaneously optimized. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that swarm optimization algorithms perform better than the other algorithms in terms of solution quality. Although the three criteria are optimized simultaneously, this optimization is independent, the optimal solution of the system is still pending, and only the best solutions are proposed. The use of evolutionary algorithms is an area of research for robotic optimization, and the following table shows some other research that has been developed in the last decade, each with different optimization objectives and, therefore, different algorithm proposals for their solution. Based on the advancements and state-of-the-are approaches presented in Table 1, the research question is as follows: Can genetic algorithms optimize the workspace and reduce cycle time in a robotic cell? Additionally, do they offer any advantages due to their ability to handle large search spaces, adapt to complex nonlinear solutions, and find near-global optimum solutions efficiently, even in dynamic and highly variable environments? In this article, a multi-objective design optimization method for the layout of workstations in a robotic cell is proposed. The robot base is considered the center of the work area, and the 2D dimensions of both the workstations and the robot base are specified. A genetic algorithm is employed to evaluate numerous possible configurations of components, selecting the most efficient ones. The objective function aims to minimize the work area while considering the interference of the stations and the robot base as constraints. The algorithm is adjusted by modifying the mutation probability. This approach enables the algorithm to minimize the work area, reduce cycle times, lower energy consumption, and maximize productivity, which would be difficult to achieve with traditional methods. Therefore, this algorithm is proposed as a design tool for production engineers and automation consultants in the industry seeking to implement robotic solutions and optimize robotic cells cost-effectively and efficiently.