Machine Learning Applied to Optimize Fuel Consumption in Amazonian Waterways Military Logistics

Bruno Alessi Castro, Pablo Gustavo Cogo Pochmann, Eduardo Borba Neves

2025

Abstract

The present study is an analysis of the use of Machine Learning tools in favor of river logistics transport in an Amazon jungle area and the impacts on the efficiency of the Logistics Commander's planning, due to a research gap identified through imprecise methods for estimating fuel consumption in logistics trips. In this way, a quantitative mathematical model was developed, using Multiple Linear Regression algorithms (due to its simplicity for operators not specialized in the area) to predict fuel consumption on logistical trips carried out by Vessel’s Center of Amazon Military Command (CECMA) vessels, using statistical data found in travel reports. After this, a comparison was made of the model found with the current modus operandi of the complement calculation completed by CECMA. applying a back test to validate the proposed model. The results obtained generated research with an R of 0.935, explaining 87% of the proposed trips. In this context, a software proposal was presented to be developed with an online interface and with the interaction of the two algorithms. Thus, the use of machine learning tools such as MLR, integrated with an AI system with feedback on predictive variables and fuel consumption of logistics missions brings an increase in the efficiency of military logistics planning and reduces costs related to fuel management after missions, contributing to the constant evolution and improvement of Military Doctrine.

Paper Citation

in Harvard Style

Castro B., Pochmann P. and Neves E. (2025). Machine Learning Applied to Optimize Fuel Consumption in Amazonian Waterways Military Logistics. In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-759-7, SciTePress, pages 226-233. DOI: 10.5220/0013461500003970

in Bibtex Style

@conference{simultech25,
author={Bruno Castro and Pablo Pochmann and Eduardo Neves},
title={Machine Learning Applied to Optimize Fuel Consumption in Amazonian Waterways Military Logistics},
booktitle={Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2025},
pages={226-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013461500003970},
isbn={978-989-758-759-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Machine Learning Applied to Optimize Fuel Consumption in Amazonian Waterways Military Logistics
SN - 978-989-758-759-7
AU - Castro B.
AU - Pochmann P.
AU - Neves E.
PY - 2025
SP - 226
EP - 233
DO - 10.5220/0013461500003970
PB - SciTePress