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The use of discrete event simulation optimisation methods is a tool commonly used as a decisionmaking support system in industrial problems, concerning management and resource allocation in order to maximise a set of values regarding costs, revenues and other enterprise interests. The present study has proposed and tested an optimisation algorithm developed on Python, with different wall clock time reduction strategies including parallelism, the Greedy Randomized Adaptive Search Procedure (GRASP) population-based metaheuristic, and ten machine learning methods. With the selected best machine learning method (Decision Trees Regressor) 6 optimisation scenarios were generated and then applied to an economic lot-size problem for a theoretical shop floor. The results showed improvements in the reduction of the processing time of 95.0 % comparing the serial GRASP with the parallel machine learning GRASP, obtaining a solution of 94.0 % of the best local optimum.

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This article objectifies the implementation of a conveyor belt in an iron ore mine. The mine operational costs of the company Vale S.A. have a tendency to increase in coming years. This increase is related to the rising price of labor, fuel, tires, maintenance costs and large transport distances. Meanwhile, environmental impacts also need to be reduced. The case study of the Fabrica mine operated by VALE in the city of Congonhas, in Minas Gerais State of Brazil, was chosen aiming for a comparative study between the employment of big trucks and the deployment of conveyor belts. The decision for the best alternative would be made by an economic analysis of the incremental cash flow. After the economic evaluation of the project, economic and environmental gains were expected for the alternative of conveyor belt deployment.

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In all mining projects, transportation costs influence net profit, justifying economic feasibility studies before transport fleet acquisition or replacement. These studies can provide the best loading and hauling equipment combination to meet production demands at lower costs by evaluating the alternatives available in the market. When there is more than one solution with similar costs and technical specifications, decision-making technics were considered to be used. Herein is presented the case of selecting hauling trucks used to transport run of mine (ROM) ore at a bauxite mining company, located in the State of Minas Gerais, Brazil, using the Multi-Criteria Decision Aid methodology (MCDA).

Hydraulic fracturing emerges currently, all over the world, as one of the more strategic techniques used by companies in the oil exploitation sector. This technique is characterized by its high productivity and profit in relation to conventional methods of hydrocarbon exploitation. However, in many countries, as is the case of Brazil, there are several divergences considering the employment of this methodology. Many renowned researchers attest that there are several irreversible environmental impacts generated by the use of this methodology. Among the main environmental impacts are the risk of groundwater level contamination, the risk of surface subsidence, and the risk of the environment contamination with fluids used in the process of the oil and gas extraction.