Solving a multi-objective model of job rotation minimizing the chemical exposure and cost by particle swarm optimization

Abstract

Today in developing countries, perilous chemicals are widely used that have harmful effect on human resources. National and global organizations developed different approaches to control chemicals exposure and their consequences in that one of them is job rotation. Since job rotation is categorized as nondeterministic polynomial-time hardness problem, meta-heuristic methods are used to solve it such as particle swarm optimization (PSO) algorithm. In this article, because job rotation can have different and contradictory objectives, the multi-objective PSO (MOPSO) method with parallel vector evaluated PSO approach is implemented. At first, a new MOPSO algorithm is presented that solves job rotation problems to minimize chemical exposures and is finally compared with non-dominated sorting genetic algorithm. The achievement of this algorithm reduces chemicals exposure in manufacturing processes such as casting, welding, foam injection, plastic injection, which ensure workers’ health.