The emergency maintenance of railway assets in the Brazilian Amazon has generated revenue losses and opportunity costs. The general objective of this study was to identify the importance of opportunity cost in decision-making for corrective and preventive maintenance strategies. The methodology proposed the modeling of the variables referring to the economic and operational data of railway maintenance in the last ten years, by non-parametric Gradient Boosting Regression Tree machine learning, and hybridizing it with the analysis of the opportunity cost for the trade-off decision making of an ore railroad in the Brazilian Amazon. The results showed that the GBDT was efficient in fitting the training data with r2 equal to one. Similarly, the test data presented satisfactory r2 values, close to one, where the degree of importance of the independent variables in the prediction of the dependent variables was obtained. Pearson's method was used to construct the correlation matrix for each pair of variables. From the generated model, eight forecast groups were created for the year 2022. Then, conflict levels were established, suggested by the economic literature, between the forecast scenarios, where the opportunity cost was identified among the alternatives with the best benefit to maintenance strategies. In this way, the opportunity cost combined with machine learning serves as an instrument to help companies in the search for better maintenance decisions, which contributes to the improvement of rail asset management.
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