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Energy-Aware Integrated Proactive Maintenance Planning and Production Scheduling

Hongliang Li, Herschel C. Pangborn, Ilya Kovalenko

Abstract

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.

Energy-Aware Integrated Proactive Maintenance Planning and Production Scheduling

Abstract

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.
Paper Structure (22 sections, 25 equations, 8 figures, 1 table, 1 algorithm)

This paper contains 22 sections, 25 equations, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. System Models
  4. Manufacturing Network Model
  5. Machine Degradation Model
  6. Machine Health Degradation
  7. Maintenance Constraints
  8. Performance-Degradation Integration
  9. Energy Consumption Model
  10. System Operational Constraints
  11. Machine and Buffer Capacity Constraints
  12. Production Flow Conservation
  13. Hierarchical Control Formulation
  14. Lower-level MPC
  15. Higher-level Bi-level Optimization
  16. ...and 7 more sections

Figures (8)

  • Figure 1: The proposed hierarchical control framework coordinates PM planning with runtime production scheduling, achieving system-level energy-efficiency improvements and energy cost savings. Dashed arrows denote information flow, while solid arrows denote maintenance execution, energy supply, and product delivery.
  • Figure 2: Hierarchical control framework for integrated PM planning and production scheduling with RTP-based energy costs in a make-to-order manufacturing environment.
  • Figure 3: Lithium-ion battery pack manufacturing system considered in the case study.
  • Figure 4: Machine schedules under the baseline controller. An orange dot indicates that the machine is under PM.
  • Figure 5: Machine schedules under the proposed framework. A green triangle indicates that the machine is under PM.
  • ...and 3 more figures