A Genetic Algorithm Based Total Productive Maintenance Model in Comparison with Neural Networks for Small and Tiny Enterprises

Abstract

In the fast growing world, Total Productive Maintenance (TPM) is the key factor to make an organization or industry succeed in a better way. There are various factors involved in total productive maintenance. These factors make major impact on the industries and all those factors are to be considered and also they should be managed well for the betterment of the industry. There are totally eight pillars in TPM, they are, Quality maintenance, Training and education, Initial flow (Early equipment management), Kobetsu-kaizen, Environmental health and safety, Autonomous maintenance, planned maintenance, Admin/office TPM. Amid these, planned maintenance is the one which increases the life of the machine, if the machines are properly maintained, to reduce the down time; or else the down time certainly reduces the availability as well as productivity of the industry. In this paper, it is intended to propose a ‘Mathematical Model’ which is utilized to predict the “optimized weight”. The technique, used is “Genetic Algorithm”, with the help of this technique we can predict the ‘Availability’ value which is closer to that of experimental value. The proposed technique is implemented in the working platform of MATLAB which gives a better result for the proposed technique. This kind of TPM model would be very much effective in small and tiny enterprises.

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