Joint Optimization of Production Scheduling and Machine Switching Under Time-of-Use Electricity Tariffs

This paper investigates an energy-efficient single-machine scheduling problem under time-of-use (TOU) electricity tariffs with machine switching decisions. With the increasing importance of demand response programs in industrial systems, electricity cost can be reduced not only by shifting production to low-price periods but also by avoiding unnecessary energy consumption during idle times. To jointly exploit these two mechanisms, a mixed-integer linear programming (MILP) model is developed to integrate job scheduling and machine switching decisions within a unified framework. The model explicitly captures processing energy consumption, idle energy consumption, and switching-related costs under time-varying electricity prices. Computational experiments based on randomly generated instances demonstrate that the proposed model can effectively reduce total energy cost. Comparative results with a two-stage strategy show that the integrated optimization framework consistently achieves lower energy consumption. Sensitivity analysis under different TOU tariff settings further confirms that the performance advantage is influenced by electricity price variations but remains robust across different scenarios. Moreover, the benefit of machine shutdown becomes more pronounced as the scheduling horizon increases. These findings highlight the importance of jointly considering load shifting and machine switching in energy-aware production scheduling and provide practical insights for improving electricity utilization efficiency in industrial systems.