One of the largest economic sectors in Brazil is the construction industry, being an important segment for investments in the country. As a result, it is a firmly established sector which has attained a high level of competitiveness. However, given the dynamic approach necessary in a world of accelerated change, the construction industry faces a wide range of challenges which must be addressed so to maintain, and if possible, improve its competitiveness. This study aims to determine how to maximize the profit of a construction company located in Curitiba, Paraná state, applying techniques of Operations Research, an analytical method of problem-solving and decision-making. The tool used to perform the optimization was Linear Programming, a mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints. The results show a strong possibility of increasing the company's profit by production leveling.
At a time when the competitive market is operating rapidly, manufacturing industries need to stay connected, have interchangeability and interoperability in their factories, ensuring that there is heterogeneous communication between sectors, people, machines and the client, challenging the manufacturing industry to discover new ways to bring new products or improve their manufacturing process. Precisely because of the need to adjust to these new market demands, factories pursue complex and quick decision-making systems. This work aims to propose applications of Machine Learning techniques to develop a decision-making platform applied to a manufacturing line reducing scrap. This goal will be achieved through a literature review in the fields of Artificial Intelligence (AI) and Machine Learning to identify core concepts for the development of a failure prediction system. This research has demonstrated the problems and challenges faced by manufacturing daily, and how, through the application of AI techniques, it is possible to contribute to assist in these problems by improving quality, performance, scrap rates and rework, through connectivity and integration of data and processes. This paper contributes to evaluate the performance of machine learning ensembles applied in a real smart manufacturing scenario of failure prediction.
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