SPECIAL ISSUE EDITORS

Kaiye Gao, Beijing Forestry University
kygao@foxmail.com

Di Wu, Central University of Finance and Economics
blessudwu@gmail.com

Rui Peng, Beijing University of Technology
pengrui1988@bjut.edu.cn

MANUSCRIPT DEADLINE
31 December, 2025

FOCUS OF THIS SPECIAL ISSUE

In the era of rapid digital transformation, Artificial Intelligence (AI) has become a key enabler in industrial engineering and economic decision-making. AI-driven solutions have shown immense potential in predictive maintenance, risk management, quality control, and system optimization. However, as industries and economies increasingly rely on AI-powered systems, ensuring their reliability, robustness, and long-term stability has become a critical challenge. The unpredictability of AI models, sensitivity to data quality, and the need for interpretable decision-making pose significant risks in high-stakes applications. Reliability engineering, traditionally rooted in statistical modeling and system design, must now integrate AI methodologies to enhance system resilience, mitigate operational risks, and improve decision-making accuracy.

 This special issue aims to bring together leading research at the intersection of AI, reliability engineering, industrial optimization, and economic decision-making. We welcome both theoretical and empirical contributions that develop innovative AI-based reliability frameworks, optimization techniques, risk assessment methodologies, and robust AI applications in industrial and economic settings. Studies integrating AI with traditional reliability modeling, interdisciplinary approaches, and novel data analytics techniques are highly encouraged.

 Topics of Interest

To be considered, papers should address key challenges at the intersection of AI and reliability in industrial engineering and economics, including but not limited to:

• AI-driven reliability assessment and predictive maintenance in industrial systems
• Optimization methods for enhancing system resilience and robustness
• AI-based risk management frameworks in industrial and economic applications
• Statistical and AI-based methods for failure detection and anomaly detection
• Data-driven reliability modeling and decision support systems
• Robust AI for uncertainty quantification and reliability-aware decision-making
• AI-enhanced quality control and fault diagnosis
• AI-driven supply chain reliability and risk mitigation

Submission Instructions

This special issue aims to provide a timely outlet for groundbreaking research on the aforementioned topics and beyond. Submissions should follow the journal’s submission guidelines. Manuscripts will be processed upon submission, and accepted papers will be published without delay. Authors are encouraged to submit as soon as their manuscripts are ready. We will strive for a swift review process upon receiving the submission.