Particle swarm optimization with mutation for the inspection allocation in reentrant production systems

Manufacturing layer-by-layer is one of the most important characteristics of reentrant production systems, in which defects are difficult to inspect after they are covered by the next layer. This study uses the particle swarm optimization (PSO) method for solving the inspection allocation problem in reentrant production systems. In the original PSO, it is very quick to find a solution, but it is easy to trap into a locally optimal solution. In this study, we add the mutation scheme borrowed from the genetic algorithm (GA) method for searching the position of optimal fitness function value from each particle. A comparison between the original PSO and PSO with mutation is made in terms of solution performance. In addition, we compare our proposed method with the GA method discussed in the literature for the inspection allocation problem in reentrant production systems, results show that the PSO method almost can find the optimal solution, and its execution time is less than that of the GA method.