Guest Editors
Pro. Chao Lu, China University of Geosciences,China
Prof. Biao Zhang, Liaocheng University, China
Prof. Lvjiang Yin, Hubei University of Automotive Technology, China
Prof. Kunkun Peng, Wuhan University of Science and Technology, China
Summary
This special issue aims to explore the application of intelligent optimization techniques in the field of green manufacturing systems, logistics, and supply chain. With the growing concerns about environmental sustainability, there is an increasing need for efficient and eco-friendly manufacturing processes. Intelligent optimization methods, such as evolutionary algorithms, machine learning, and swarm intelligence, have shown promising potential in addressing the challenges of green manufacturing. This special issue invites researchers and practitioners to contribute their original research, case studies, and review articles, focusing on the development and application of intelligent optimization algorithms and their integration into various aspects of green manufacturing, logistics, and supply chain. The goal is to provide insights into the latest advancements, identify key trends, and promote the adoption of intelligent optimization techniques to achieve sustainable and environmentally conscious production practices. Contributions from interdisciplinary perspectives are encouraged to foster collaboration and exchange of ideas among researchers, engineers, and policymakers. Potential topics include but are not limited to intelligent optimization algorithms for sustainable manufacturing or green scheduling in various shops, dynamic shop scheduling, energy-efficient shop scheduling, low-carbon shop scheduling, and optimization problems in semiconductors, iron, automobile, chemical industry, etc.
Keywords
green manufacturing; intelligent manufacturing; green logistics and supply chain; intelligent optimization algorithms; multi-objective scheduling; energy-efficient; low-carbon
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