Prediction of Engineering Manpower using Neural Network and Genetic Algorithm
J. Paul Choudhury, Bijan Sarkar, Sanat Kumar Mukherjee · IETE Journal of Research · 2002
The smooth working of industry depends on the availability of proper engineering manpower. If properly qualified and experienced technical personnel are not available, the industry cannot run in the most efficient way. Here an effort is made to assess the engineering manpower requirement (personnel belonging to mechanical engineering) in certain industry group (Steel Manufacturing) in the state of West Bengal in India for the next five years. An approach consists of artificial neural network preferably feed forward back propagation with genetic algorithm is tested and used for the assessment of engineering manpower. In this approach the data is initially fuzzified, and the fuzzified data is used to train an artificial neural network. The output of the trained neural network is defuzzified and the operators of genetic algorithm (GA) are applied on the defuzzified data until the average error lies below a particular value. Certain statistical functions i.e. linear, exponential, curvilinear (parabolic) equations and the tables of orthogonal polynomial are applied on the estimated data based on the proposed approach. Based on the minimum average error, the particular statistical model is chosen and used for the assessment of futuristic engineering manpower.