An economic policy for noise control in industry using genetic algorithm

Noise control in industrial workplaces is enforced by health and safety regulations in order to prevent or reduce risks to personnel. Apart from compliance with rules, the adverse effects of noise on productivity have always been a challenge for industry. As a consequence, practical solutions, ranging from protection aids to acoustic damping and isolation, have occasionally been employed. These unplanned remedies do not necessarily aim at higher risk locations and hence may impose significant and unjustified expense on the company. In this paper, the optimum combination of treatments is investigated using binary integer programming with objective cost function. The model constraints include recommended noise doses for highly exposed operators as well as budget limits. In addition, sound specification of the sources, treatment effects and relevant production information are incorporated into the model through structured databases. Then a genetic algorithm is utilized in a Matlab environment and final results are obtained. The procedure is applied to an example of a press shop and the validity of the results is approved.