SUPPORTING TOOLS FOR OPTIMIZATION AND SENSITIVITY ANALYSIS OF A FLEXIBLE AGE REPLACEMENT POLICY FOR TECHNICAL SYSTEMS

Maintenance management is essential to ensure the effective performance of technical systems, and choosing the right maintenance policy is crucial in this process. This article presents two tools developed to support decisions in the implementation of a flexible age substitution policy, called the STZ policy. The tools are intended for STZ policy sensitivity optimization and analysis. For optimization, a hybrid algorithm of Genetic Algorithm and Particle Cloud Optimization was used. For the sensitivity analysis, an algorithm was developed with an approach based on Monte Carlo simulation. The tools, in the prototype phase, have a simple interface, which facilitates their use by analysts and maintenance managers, thus contributing to the dissemination of the STZ policy in practical contexts.