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Open AccessArticle
Low-Carbon and Energy-Efficient Dynamic Flexible Job Shop Scheduling Method Towards Renewable Energy Driven Manufacturing
by
Yao Lu
Yao Lu
,
Qicai Zhu
Qicai Zhu
Changhao Tian
Changhao Tian
Erbao He
Erbao He
Taihua Zhang
Taihua Zhang
1
School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550001, China
2
Technical Engineering Center of Manufacturing Service and Knowledge Engineering, Guizhou Normal University, Guiyang 550001, China
*
Author to whom correspondence should be addressed.
Machines 2026, 14(1), 88; https://doi.org/10.3390/machines14010088 (registering DOI)
Submission received: 22 November 2025
/
Revised: 5 January 2026
/
Accepted: 9 January 2026
/
Published: 10 January 2026
Abstract
As one of the major sources of global carbon emissions, the manufacturing industry urgently requires green transformation. The utilization of renewable energy in production workshop offers a promising route toward zero-carbon manufacturing. However, renewable energy fluctuations and dynamic workshop events make efficient scheduling increasingly challenging. This paper introduces a low-carbon and energy-efficient dynamic flexible job shop scheduling problem oriented towards renewable energy integration, and develops a multi-agent deep reinforcement learning framework for dynamic and intelligent production scheduling. Inspired by the Proximal Policy Optimization (PPO) algorithm, a routing agent and a sequencing agent are designed for machine assignment and job sequencing, respectively. Customized state representations and reward functions are also designed to enhance learning performance and scheduling efficiency. Simulation results demonstrate that the proposed method achieves superior performance in multi-objective optimization, effectively balancing production efficiency, energy consumption, and carbon emission reduction across various job shop scheduling scenarios.
Share and Cite
MDPI and ACS Style
Lu, Y.; Zhu, Q.; Tian, C.; He, E.; Zhang, T.
Low-Carbon and Energy-Efficient Dynamic Flexible Job Shop Scheduling Method Towards Renewable Energy Driven Manufacturing. Machines 2026, 14, 88.
https://doi.org/10.3390/machines14010088
AMA Style
Lu Y, Zhu Q, Tian C, He E, Zhang T.
Low-Carbon and Energy-Efficient Dynamic Flexible Job Shop Scheduling Method Towards Renewable Energy Driven Manufacturing. Machines. 2026; 14(1):88.
https://doi.org/10.3390/machines14010088
Chicago/Turabian Style
Lu, Yao, Qicai Zhu, Changhao Tian, Erbao He, and Taihua Zhang.
2026. "Low-Carbon and Energy-Efficient Dynamic Flexible Job Shop Scheduling Method Towards Renewable Energy Driven Manufacturing" Machines 14, no. 1: 88.
https://doi.org/10.3390/machines14010088
APA Style
Lu, Y., Zhu, Q., Tian, C., He, E., & Zhang, T.
(2026). Low-Carbon and Energy-Efficient Dynamic Flexible Job Shop Scheduling Method Towards Renewable Energy Driven Manufacturing. Machines, 14(1), 88.
https://doi.org/10.3390/machines14010088
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