Minimization of path lengths for materials and workers in smart factories using particle swarm optimization algorithm.

In smart factories, where humans collaborate with robots and machines, productivity can be optimized by minimizing the distance traveled by workpieces and workers. Previous studies on this subject have largely focused on the movement of the workpieces. Therefore, this study expands on this work by accounting for the movement of the workers. In this study, the facility layout of smart factories is optimized by minimizing the distances traveled by the workpieces and workers, which is accomplished through the particle swarm optimization (PSO) algorithm. In this algorithm, each arrangement of the factory elements constitutes a unique particle, and initial particles are generated randomly using rules to be used as seeds for optimization. PSO is then executed to generate a layout that minimizes the distances traveled by the workpieces and workers. This method produces layouts that are more optimal than those in which only the distance traveled by the workpieces is considered. [ABSTRACT FROM AUTHOR]