## 3 Materials and Methods
### Integration of Components in the APM Prototype  
Integrating modules in the APM prototype represents a complex and multifaceted process involving combining various techniques and approaches to develop an effective decision support tool in painting planning for offshore platforms. This process can be broken down into five primary stages: variable survey, code structuring, definition of the data flow, implementation of optimisation models, and integration of the components in the graphical interface. The implementation leverages several specialised Python libraries: Pandas [83] for data manipulation and analysis of inspection and maintenance records, PuLP [84] for implementing the linear programming optimisation models that drive the different maintenance strategies, sci-kit-learn [85] for advanced analytics and corrosion prediction models, and PySimpleGUI [86] for creating an intuitive user interface. Each strategy is formulated as a linear programming problem with objective functions, the direction of optimisation, decision variables, and appropriate constraints [87]. This integrated approach efficiently handles complex maintenance planning scenarios while providing an accessible interface for maintenance engineers and planners. The variable survey constitutes a crucial stage for problem modelling, which involves identifying, classifying, and collecting data from competent parties. At this stage, it is fundamental to understand the variables' limits and their relationships and to verify their dependence or independence [88]. Another critical aspect of integration is the definition of the data flow, which involves the import, transformation, and treatment of inspection and regulatory demand data, as well as the calculation of key indicators such as corrosion progress and criticality. Implementing optimisation models involved developing different strategies (PH limit, corrosion integrity, regulatory demand, and criticality) that met the specific needs of painting planning. Finally, the integration of modules in the graphical interface was carried out by developing an intuitive and easy-to-use interface, which guides the user through the stages of data input, exploratory analysis, simulation configuration, and results evaluation, as shown in Figure 3. The optimisation models and the results in visualisation dashboards are seamlessly integrated into the interface, allowing users to interact with the prototype's functionalities. The system's structure is designed to capture and process inspection data in various formats, integrating relevant information on corrosion levels, environmental conditions, and regulatory constraints. These data are entered into the system through an interface that allows for the uploading and validating of inspection sheets and specific conditions for each platform, ensuring that the analysis starts from a standardised and consistent basis. Subsequently, the system processes this data using predictive models and multi-criteria analysis techniques, generating visualisations and key metrics, such as the corrosion index and regulatory demands, to facilitate decision-making for maintenance planning. The optimisation model in this study managed each maintenance strategy individually as a single-objective process, allowing each strategy to be executed independently according to the established priority. The system does not automatically perform the selection of the painting planning strategy; rather, it depends on managerial decision-making, where the trade-off among objectives, such as the intervention time and attention to high-criticality areas, is evaluated. In this context, each strategy is executed according to the selected objective function, such as minimising person-hours or prioritising areas requiring more significant corrosion intervention. This approach allows the management to externally decide which strategy best fits each situation's specific objectives and regulatory demands.