ABSTRACT
The present work is carried out in a brick manufacturing (BM) unit near Hathras, India. BM is labor intensive in general and comprises the following major jobs – molding, loading, stacking, covering, firing and unloading. Firing, the most severe job, involves undue exposure of workers to excessive heat. Moreover, they subject themselves to extreme work conditions by working extra hours to maximize their earnings due to economic reasons, and hence are exposed to greater risk of heat stress. To manage the risk of heat stress, we implement a job-combination approach wherein firing workers do another job (molding in this work) along with firing job. We measure the risk of heat stress in terms of composite discomfort score (CDS), computed using factor rating, a method popularly used in location planning decisions. CDS is a direct indicative of perceived discomfort level of workers. Further, we employ hybrid meta-heuristic (HMH), an evolutionary multi-objective optimization (EMO) technique, to search for optimal CDS-earning trade-off (CET) solutions with two conflicting objectives, viz. minimization of CDS, and maximization of earnings.
