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

Research output: Contribution to journalConference articlepeer-review

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 integration. 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. Market response is incorporated through price elasticity, enabling strategic pricing to align the product demand with the availability of renewable energy. 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.

Original languageEnglish (US)
Pages (from-to)665-670
Number of pages6
JournalIFAC-PapersOnLine
Volume59
Issue number30
DOIs
StatePublished - Oct 1 2025
Event5th Conference on Modeling, Estimation and Control, MECC 2025 - Pittsburgh, United States
Duration: Oct 5 2025Oct 8 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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