Submission Deadline: 31 December 2026 View: 287 Submit to Special Issue
Prof. Dr. Chia-Nan Wang
Email: cn.wang@nkust.edu.tw
Affiliation: Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Research Interests: operations management, decision-making, management of technology, electronic commerce, systematic innovation
Dr. Phan Van-Thanh
Email: pvthanh.tg@vku.udn.vn
Affiliation: Vietnam-Korea University of Information and Communication Technology, Danang, Vietnam
Research Interests: grey system theory, time series forecasting, decision making, data envelopment analysis, measurement, operations management
The rapid transition toward Industry 4.0, interconnected global supply chains, and complex cyber-physical systems has introduced unprecedented challenges into engineering and management operations. Today's decision-makers are increasingly confronted with large-scale, highly dynamic, and non-linear optimization problems under real-world uncertainties. Traditional exact mathematical methods and conventional scheduling algorithms often struggle to meet the real-time and high-dimensional demands of these modern systems. Consequently, the integration of advanced computer modeling, artificial intelligence, and operations research has emerged as a critical research frontier. This Special Issue aims to explore the convergence of novel algorithmic innovations with practical smart system architectures. We invite original research and comprehensive review articles that propose data-driven models, deep reinforcement learning frameworks, hybrid metaheuristics, and digital twin-driven simulations tailored for complex scheduling tasks. By bridging theoretical computational sciences with real-world applications—such as smart manufacturing, logistics, and energy management—this Special Issue seeks to provide robust, intelligent, and sustainable solutions to the most challenging optimization bottlenecks across diverse industrial sectors.
Topics
• Algorithmic Innovation & AI
• Smart Manufacturing & Industry 4.0/5.0
• Uncertainty & Resilience
• Hybrid Approaches
• Logistics & Transportation
• Sustainability & Energy
• Distributed Computing
• Advanced Operations Research
• Intelligent scheduling and optimization
• Robust, stochastic, and decision-making optimization
• Intelligent management and optimization
• Energy-efficient, green, and sustainable scheduling
• Resource-constrained project scheduling and optimization
• Human-robot collaborative scheduling and optimization
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