An intelligent system to address occupational health of workers exposed to high risk jobs

Abstract

Workers in labor-intensive manufacturing units, in general, maximize their earnings by subjecting themselves to greater risk of occupational health hazards (RoOHH) mainly due to economic reasons. To embark upon this issue, we introduce an intelligent system employing artificial neural network (ANN) and non-dominated sorting genetic algorithm (NSGA-II). Experimentations are carried out in a brick manufacturing unit in India. Observations spell out that firing is the most severe job among others. A job-combination approach is incorporated wherein firing workers do another job along with firing to reduce their exposure to high temperature zone while maintaining their earnings to a satisfactory level. RoOHH is measured in terms of risk assessment score (RAS). ANN models the psychological responses of workers in terms of RAS, and facilitates the evaluation of one of the fitness function of NSGA-II. NSGA-II searches for optimal work schedules in a job-combination to minimize RAS and maximize earnings simultaneously.