This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle Greentelligent Scheduling for Energy-Efficient Aluminum Extrusion Casting: A Multi-Objective Optimization Approach 1 State Key Lab of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China 2 Zhejiang Key Laboratory of Aerospace Metallic Materials, Hangzhou City University, 310015, China * Author to whom correspondence should be addressed. Energies 2026, 19(12), 2743; https://doi.org/10.3390/en19122743 (registering DOI) Submission received: 9 May 2026 / Revised: 29 May 2026 / Accepted: 4 June 2026 / Published: 7 June 2026 Abstract This study introduces a greentelligent scheduling approach to enhance energy efficiency in the aluminum extrusions casting workshop (ACW), addressing the high energy consumption and low efficiency inherent in these processes. Global energy consumption is significantly attributed to the manufacturing sector, with aluminum extrusions being one of the most common products, particularly in energy-intensive casting workshops. Given the considerable demand and potential for energy savings in aluminum extrusions manufacturing (AEM), this study proposes an intelligent scheduling approach to minimize non-processing energy consumption (NPE) while also reducing average completion time (ACT). Utilizing industrial internet of things (IIoT) technologies, practical production data is acquired to support a bi-objective scheduling model. An empirical knowledge-based evolution algorithm (EBA) with an improvement strategy (SO-EBA) is developed to efficiently solve this complex, NP-hard problem. A production case in an ACW demonstrates the effectiveness of the SO-EBA. Compared to benchmark algorithms, the SO-EBA achieves significant reductions in optimal NPE by more than 39.41%, while maintaining production efficiency. This work advances greentelligent manufacturing by integrating IIoT and intelligent algorithms, offering a scalable solution for sustainable production in energy-intensive industries. Keywords: Share and Cite MDPI and ACS Style Peng, C.; Peng, S.; Krissyda, D.; Song, C.; AL-Bukhaiti, K.; Wan, A. Greentelligent Scheduling for Energy-Efficient Aluminum Extrusion Casting: A Multi-Objective Optimization Approach. Energies 2026, 19, 2743. https://doi.org/10.3390/en19122743 AMA Style Peng C, Peng S, Krissyda D, Song C, AL-Bukhaiti K, Wan A. Greentelligent Scheduling for Energy-Efficient Aluminum Extrusion Casting: A Multi-Objective Optimization Approach. Energies. 2026; 19(12):2743. https://doi.org/10.3390/en19122743 Chicago/Turabian Style Peng, Chen, Shuai Peng, Dimas Krissyda, Ci Song, Khalil AL-Bukhaiti, and Anping Wan. 2026. "Greentelligent Scheduling for Energy-Efficient Aluminum Extrusion Casting: A Multi-Objective Optimization Approach" Energies 19, no. 12: 2743. https://doi.org/10.3390/en19122743 APA Style Peng, C., Peng, S., Krissyda, D., Song, C., AL-Bukhaiti, K., & Wan, A. (2026). Greentelligent Scheduling for Energy-Efficient Aluminum Extrusion Casting: A Multi-Objective Optimization Approach. Energies, 19(12), 2743. https://doi.org/10.3390/en19122743 Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here. Article Metrics Article metric data becomes available approximately 24 hours after publication online.
