An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs

The industrial sector is the largest consumer of total energy in the world, with the majority of its consumption in the form of electricity. Recently, to strengthen the capacity building of peak load regulation, many countries have implemented time-of-use (TOU) tariffs to encourage manufacturing enterprises to shift their electricity con-sumption from on-peak hours to mid-peak and off-peak hours. It brings the urgent requirement for energy -efficient operation and maintenance (O&M) schedules in manufacturing enterprises, among which the mainte-nance planning and production scheduling (MPPS) are both highly correlated to electricity consumption under this time-varying electricity charging modes. To tackle this key issue, a complex MPPS problem for serial-parallel manufacturing systems under TOU tariffs is studied in this paper. To solve the problem, an energy-efficient two -stage maintenance (ETM) strategy is developed to minimize the sum of the total electricity cost and tardiness cost. In the first stage, the preventive maintenance (PM) planning is presented to obtain the multi-attribute PM intervals for each machine, considering machine availability, maintenance cost, and the potential impact of planned PM actions on average electricity price. Based on the PM intervals, a mixed-integer programming model is proposed for a hybrid flow shop schedule with PM actions in the second stage. Finally, the results confirm the effectiveness of this ETM strategy in achieving electricity cost savings and ensuring system productivity, which can provide instructions for the operation of industrial enterprises.

Automated summary

In this research, a complex maintenance planning and production scheduling problem for serial-parallel manufacturing systems under time-of-use tariffs is studied. An energy-efficient two-stage maintenance strategy is developed, which aims to minimize the total electricity cost and tardiness cost. The strategy includes preventive maintenance planning in the first stage and a mixed-integer programming model for scheduling with maintenance actions in the second stage. The results demonstrate the effectiveness of this strategy in reducing electricity costs and ensuring system productivity, providing guidance for industrial enterprises.