Energy-Aware Integrated Proactive Maintenance Planning and Production Scheduling
This work addresses energy management and maintenance trade-offs for manufacturers, offering a practical solution to reduce operational costs, though it is incremental in integrating existing methods.
The paper tackles the challenge of balancing energy cost savings from real-time pricing with machine degradation in manufacturing by proposing a hierarchical bi-level control framework that jointly optimizes proactive maintenance planning and production scheduling, demonstrating in a case study that it reduces energy costs while meeting production targets.
Demand-side energy management, such as the real-time pricing (RTP) program, offers manufacturers opportunities to reduce energy costs by shifting production to low-price hours. However, this strategy is challenging to implement when machine degradation is considered, as degraded machines have decreased processing capacity and increased energy consumption. Proactive maintenance (PM) can restore machine health but requires production downtime, creating a challenging trade-off: scheduling maintenance during low-price periods sacrifices energy savings opportunities, while deferring maintenance leads to capacity losses and higher energy consumption. To address this challenge, we propose a hierarchical bi-level control framework that jointly optimizes PM planning and runtime production scheduling, considering the machine degradation. A higher-level optimization, with the lower-level model predictive control (MPC) embedded as a sub-problem, determines PM plans that minimize total operational costs under day-ahead RTP. At runtime, the lower-level MPC executes closed-loop production scheduling to minimize energy costs under realized RTP, meeting delivery targets. Simulation results from a lithium-ion battery pack assembly line case study demonstrate that the framework strategically shifts PM away from bottlenecks and high-price hours, meeting daily production targets while reducing energy costs.