Jiacun Wang, Ph.D., professor in the Department of Computer Science and Software Engineering, recently co-published the article, “Large Language Model-Assisted Reinforcement Learning for Hybrid Disassembly Line Problem” in MDPI’s Mathematics Journal (Vol. 12, Issue 24, Dec. 2024).
The article explores the challenges of the disassembly line balancing problem (DLBP) and offers potential solutions to aid in the recycling of end-of-life products, thereby reducing environmental impact and promoting resource reuse. The authors weigh previously overlooked factors, such as worker fatigue and hybrid assembly lines, when conducting their research which offers news insights into the application of large language models (LLM) in reinforcement learning and DLBP.
The article was co-authored by Xiwang Guo, Chi Jiao, Peng Ji, and Xianming Lang, Liaoning Shihua University, China; Shujin Qin, Shangqiu Normal University, China; Bin Hu, Kean University, NJ; and Liang Qi, Shandong University of Science and Technology, China.