## 1 Introduction
Recent research has explored advanced algorithms, such as mixed-integer programming and evolutionary algorithms, to address complex optimization problems. Genetic algorithms (GA) have been successfully applied to solve production flow problems with blocking constraints and variable processing times [19], as well as hierarchical planning focused on optimizing postures and movements [20]. In [21], a heuristic algorithm optimizes the placement of workstations, modeling the robot through homogeneous transformation matrices, representing components with cylindrical envelopes, and identifying collisions via 2D circular projections. Trajectories are segmentally adjusted after relocating interference points. Furthermore, approaches based on GA, particle swarm optimization (PSO), ant colony optimization (ACO), and differential evolution have proven effective in optimizing criteria such as workspace layout,operational time, and system manipulability, with PSO algorithms standing out for their high-quality solutions [22]. Evolutionary algorithms (EA), however, present certain limitations, including high computational costs, reliance on well-defined objective functions, and challenges in handling constraints. Additionally, they may produce solutions that are difficult to interpret, restricting their effectiveness and applicability in complex or resource-constrained environments. For instance, an algorithm might suggest placing two workstations at opposite corners of the cell to minimize interferences, but this layout could be impractical for human supervision or maintenance access. The quality of results is closely tied to the proper selection of parameters, such as population size, mutation rates, and crossover rates. Nevertheless, they excel at exploring extensive solution spaces, even in complex and nonlinear problems with multiple variables, demonstrating effectiveness in scenarios with uncertain or dynamically changing parameters [23]. While simulations enable visualization of system behavior, facilitating the identification of issues such as collisions, inefficient trajectories, or bottlenecks, EA stands out for their capacity for global exploration and optimization. Several authors suggest that combining both tools may lead to better results [21; 22]. This article proposes an integrated approach for optimizing the design of workspaces and cycle time in RC, addressing the limitations of current methods and tools. The approach involves using GA to optimize workspace distribution by determining the optimal placement of workstations, followed by an ACO algorithm to identify the shortest process trajectory. Together, these algorithms form an accessible design tool that does not require advanced knowledge for implementation, reducing reliance on highly specialized experts. The method is implemented and evaluated in practical industrial scenarios, with results validated using Mitsubishi's RT TOOLBOX3 PRO simulation software version 2.5C [24]. Additionally, innovative criteria are introduced for evaluating RC designs, emphasizing flexibility, resilience, and reconfigurability. This approach integrates emerging technologies with advanced algorithms to deliver scalable, efficient, and practical solutions applicable to both academic research and industrial operations.