Modeling and Multiobjective Optimization for Energy-Aware Hybrid Flow Shop Scheduling

In this paper, a multiobjective scheduling problem for energy-aware Hybrid Flow Shop (HFS) is studied, in which minimal makespan and energy consumption are set as the objectives. The energy consumption model of HFS is established, in which the energy consumption is categorized into five parts as Processing Energy (PE), Adjusting Energy (AE), Transport Energy (TE), Waiting Energy (WE) and Routine Energy (RE). Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm (NSGA-2) are applied to obtain optimal schedules. Simulation results demonstrate that the proposed method is effective in supporting energy efficiency management in HSF.