Artificial Intelligence and Optimization in Sustainable Manufacturing

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Guest Editors
Prof. Gao Liang
Huazhong University of Science & Technology, China
Email: gaoliang@hust.edu.cn

Prof. Congbo Li
Chongqing University, China
Email: congboli@cqu.edu.cn

Prof. Xinyu Li
Huazhong University of Science & Technology, China
Email: lixinyu@mail.hust.edu.cn

Dr. Lingling Li
Southwest University, China
Email: lingzithyme@swu.edu.cn

Manuscript Topics
A new scale of urbanization, digitization, and industrialization has been driving the change of our world more profoundly than ever before. At a historic time when reducing the environmental impacts of industrial sectors is considered one of the cornerstones of relations between people and the environment, sustainability is noticeably becoming a major component of the missions for manufacturing engineers and scientists to stay globally competitive. The idea of “Sustainability” applies the creation of manufactured products that use processes that are non-polluting, conserve energy and natural resources, and are economically sound and safe for employees, communities and consumers.

With the recent advancement of artificial intelligence (AI), various intelligent innovations have emerged and significantly expanded the scopes of sustainable manufacturing technologies in modeling, analysis, real-time monitoring, intelligent optimization and control for manufacturers. The goal of this special issue is to bring together researchers in academic institutions and professionals in sustainable manufacturing into a forum, to show the state-of-the-art research and applications by presenting efficient scientific and engineering solutions, addressing the needs and challenges for integration with new advanced technologies, and providing visions for future research and development.

This special issue will publish original research, review and application papers including but not limited to the following fields:

• Green product design based on Big Data and AI techniques
• Big data enabled modeling, analysis, intelligent control and optimization for energy-efficient manufacturing systems
• Intelligent optimization algorithms for environmentally-conscious production and service: planning, scheduling and control
• AI based on-line monitoring, data analytics, modeling and optimization for low-carbon manufacturing
• Real-time energy resource management for manufacturing systems through Artificial Intelligence and Big Data
• Problem diagnosis for sustainable manufacturing via machine learning
• Applications of big data, IoT and artificial intelligence for remanufacturing
• Big Data and AI driven optimization methods for green supply chain
• Life-cycle assessment of green products/processes by integrating Big Data and AI methods

Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is June 30, 2019

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Chengyu Hu, Junyi Cai, Deze Zeng, Xuesong Yan, Wenyin Gong, Ling Wang
+ Abstract     + HTML     + PDF(1069 KB)
Xiaoke Li, Fuhong Yan, Jun Ma, Zhenzhong Chen, Xiaoyu Wen, Yang Cao
+ Abstract     + HTML     + PDF(758 KB)
Zhi-xin Zheng, Jun-qing Li, Hong-yan Sang
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Jun Zheng, Zilong li, Bin Dou, Chao Lu
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