Improved parallel reactive hybrid particle swarm optimization using improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory

This study proposes improved parallel reactive hybrid particle swarm optimization (IPRHPSO) using an improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory. Conventionally, in an energy plant, fixed loads of various tertiary energies have been utilized to solve the optimal operational planning of an energy plant so far. Additionally, in production lines, only the minimization of production time has been yet considered. Therefore, the secondary energy cost of a factory cannot be reduced accurately. However, in this study, the loads of various tertiary energies are calculated according to the candidates of production scheduling and the optimal operational planning of an energy plant is determined using the tertiary energies. This can explicitly reduce the secondary energy cost of a factory. The proposed method was applied to 10 jobs and machine JSPs each. Accordingly, it was verified that it can minimize the secondary energy cost and production time simultaneously, and realize fast computation through parallel computation using IPRHPSO.