## 1 Introduction
[11] determined two possible layout solutions: an axial layout solution and a compact layout solution, which were obtained through the application of a meta-design methodology after the establishment of direct and indirect relationships between the various space units, whereas Gomez, Tascon, and Ayuga [12] proposed several types of layouts, which were obtained through the use of the systematic layout planning method, taking into account the continuity of the established production process as well as the criteria of product quality and hygiene, noise, smells, accessibility, hygiene and safety at work, and the difference between wet and dry areas. Despite the feasible results provided by these methodologies, due to the inclusion of additional variables, such as environmental changes, they can become complex and time consuming. For this reason, heuristics or meta-heuristics are often used to obtain near optimal solutions, providing flexibility and the capacity for adaptation as the environment changes. The most common computational intelligence algorithms to solve the problem of layout optimization are genetic algorithms (GAs), which are particularly suitable for solving complex optimization problems and, consequently, are effective for applications requiringadaptive troubleshooting strategies [13]. The theoretical basis for GAs was proposed by John Holland in 1975 [14]. The GA is a model that reproduces some evolutionary biological theories in the resolution of optimization problems [15]. Numerous applications for GA have been proposed to solve the facility site layout problem. RazaviAlavi and Abou Rizk [16] proposed an integrated simulation model based on a genetic algorithm to estimate the minimum layout cost, taking into account restrictions such as required distances for safety or accessibility between facilities. To define the ideal design for a distribution system in a logistic distribution centre, Chen et al. [17] suggested a method based on a genetic algorithm that allows for the capacity maximization of a distribution system with multiple charging stations. Liu et al. [18] applied a genetic algorithm to innovatively optimize offshore wind farm layouts under different seabed terrains, increasing the total energy output, while Krajcovic et al. [19] presented a specific genetic algorithm layout planner (GALP) that uses a genetic algorithm to optimize the spatial arrangement. Said and El-Rayes [20] proposed and compared two global optimization models of dynamic site layout planning, where the first model utilized GAs, while the second model was based on an approximate dynamic programming. Despite the fact that the occupational health and safety field has always followed revolutionary developments in the industry, reacting positively to technological progress and changes, leading to reliable and sustainable solutions, there is a lack of research work based on facility layout problems that include the occupational risk aspect [21]. Thus, this work aimed to contribute to filling gap in the literature by proposing a different method based on a genetic algorithm to solve a facility layout problem considering the occupational risk component, allowing for the mitigation of occupational risks allied to a higher level of operational process efficiency. Therefore, the objectives of this study are summarized as the following:  
* Identify and quantify two categories of risks related to the wine sector:
* General;
* Ergonomic. * Implement a method to optimize wine industry layouts with the view of occupational risk mitigation. To achieve these objectives, this study integrated two methods of risk assessment based on:  
* The William T. Fine method for general risk;
* Metabolic energy expenditure for ergonomic risks. In a subsequent step, an approach based on a genetic algorithm provides the optimization of the layout through a spatial reorganization according to the distances between the different areas of the layout, taking into account the results of the risk assessment.