Bi-level Model Predictive Control for Energy-aware Integrated Product Pricing and Production Scheduling

TL;DR

This paper introduces a bi-level model predictive control framework that optimizes product pricing and production scheduling to enhance sustainability and profitability by leveraging renewable energy and real-time electricity prices.

Contribution

It presents a novel bi-level control approach integrating renewable energy considerations into product pricing and scheduling for manufacturing.

Findings

Reduces grid energy costs in manufacturing.

Increases profit through optimized pricing and scheduling.

Enhances renewable energy utilization.

Abstract

The manufacturing industry is under growing pressure to enhance sustainability while preserving economic competitiveness. As a result, manufacturers have been trying to determine how to integrate onsite renewable energy and real-time electricity pricing into manufacturing schedules without compromising profitability. To address this challenge, we propose a bi-level model predictive control framework that jointly optimizes product prices and production scheduling with explicit consideration of renewable energy availability. The higher level determines the product price to maximize revenue and renewable energy usage. The lower level controls production scheduling in runtime to minimize operational costs and respond to the product demand. Price elasticity is incorporated to model market response, allowing the system to increase demand by lowering the product price during high renewable energy generation. Results from a lithium-ion battery pack manufacturing system case study demonstrate that our approach enables manufacturers to reduce grid energy costs while increasing profit.