Energy-Aware Model Predictive Control for Batch Manufacturing System Scheduling Under Different Electricity Pricing Strategies

TL;DR

This paper introduces an energy-aware MPC framework for manufacturing scheduling that adapts to variable electricity prices, reducing costs while maintaining production efficiency and constraints.

Contribution

It develops a novel network-based model and formulates a MIQP for dynamic scheduling under different electricity pricing schemes, which is a new approach in energy-aware manufacturing.

Findings

Reduces energy costs significantly across pricing schemes

Maintains production goals and constraints effectively

Provides insights into electricity pricing impacts on manufacturing

Abstract

Manufacturing industries are among the highest energy-consuming sectors, facing increasing pressure to reduce energy costs. This paper presents an energy-aware Model Predictive Control (MPC) framework to dynamically schedule manufacturing processes in response to time-varying electricity prices without compromising production goals or violating production constraints. A network-based manufacturing system model is developed to capture complex material flows, batch processing, and capacities of buffers and machines. The scheduling problem is formulated as a Mixed-Integer Quadratic Program (MIQP) that balances energy costs, buffer levels, and production requirements. A case study evaluates the proposed MPC framework under four industrial electricity pricing schemes. Numerical results demonstrate that the approach reduces energy usage expenses while satisfying production goals and adhering to production constraints. The findings highlight the importance of considering the detailed electricity cost structure in manufacturing scheduling decisions and provide practical insights for manufacturers when selecting among different electricity pricing strategies.