This research deals with the development of an optimization system to minimize employee noise exposure in the work environment. It is known from the literature that continuous exposure to high noise levels can cause heart overload, stress, fatigue, and increase accident numbers at a production line. Thus, it is necessary to develop acoustic solutions at an industrial level that could minimize failures and accident occurrences. The rules that regulate occupational noise exposures allow an assessment of the degrees of exposure and subsequent corrections of working conditions. It is observed that the exposure is necessary for further evaluation and correction. Therefore, this research proposes to simulate occupational noise exposure conditions through mathematical models implemented in C++, using the GUROBI linear optimization package and to act previously to minimize ONIHL (Occupational Noise-Induced Hearing Loss). One of this work results is based on Doses Values, TWA (Time Weighted Average) and Distances Covered, using these three factors simultaneously through the optimization, it obtains a route that minimizes exposure and avoids ONIHL. Although there is a need for balanced doses between employees, to this end, the Designation Problem was implemented. Thus, with the routes obtained by optimization, an efficient allocation task was made for the maintenance crew, resulting in minimized and balanced doses. This model was applied to a real industrial plant that will not be identified, only methodology and results obtained will be presented.

In this paper, the applications of the multivariate data analysis and optimization on vibration signals from compressors have been tested on the assembly line to identify nonconforming products. The multivariate analysis has wide applicability in the optimization of weather forecasting, agricultural experiments, or, as in this case study, in quality control. The techniques of discriminant analysis and linear program were used to solve the problem. The acceleration and velocity signals used in this work were measured in twenty-five rotating compressors, of which eleven were classified as good baseline compressors and fourteen with manufacturing defects by the specialists in the final acoustic test of the production line. The results obtained with the discriminant analysis separated the conforming and nonconforming groups with a significance level of 0.01, which validated the proposed methodology.