O papel da Inteligência Artificial na otimização da manutenção de frotas de caminhões para o transporte de madeira: uma RBS

Abstract

Currently, fleet management for wood transport faces severe challenges, such as operations in remote areas and high terrain severity, which limit traditional maintenance approaches. This article presents a Systematic Literature Review (SLR) on how Artificial Intelligence (AI) acts in the optimization of these processes. The research was conducted across the Scopus, IEEE Xplore, and Web of Science databases, covering the period from 2015 to 2025, resulting in a final portfolio of 31 validated articles after screening 258 raw records. The results demonstrate that Deep Learning techniques, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), are considered benchmarks in Remaining Useful Life (RUL) prognosis, showing accuracy levels exceeding 95%. It is evident that the integration of Edge Computing architectures is fundamental to overcoming low connectivity in the forestry sector. Thus, it was found that AI not only increases fleet availability but also serves as a strategic element for reducing operational costs and strengthening Environmental, Social, and Governance (ESG) practices in the industry.