Yi-Kuei Lin, National Yang Ming Chiao Tung University

Industrial engineering systems, including manufacturing systems, supply chains, logistics networks, energy and power systems, transportation systems, and service operations, play a critical role in modern society. The reliable operation of such systems is essential for ensuring efficiency, safety, and robustness in the presence of uncertainty, disruptions, and complex interactions among system components. Failures or performance degradation in these systems may lead to significant economic losses, operational inefficiencies, and societal impacts.

Traditionally, reliability modeling in industrial engineering has been grounded in probabilistic analysis, stochastic modeling, and analytical evaluation techniques. While these approaches remain fundamental, recent advances in sensing technologies, industrial Internet of Things, and information systems have enabled the collection of large-scale operational and condition-monitoring data. This has led to a growing interest in data-driven and intelligent reliability modeling, where statistical learning, machine learning, and other statistical analytical approaches complement classical reliability theory.

At the same time, reliability analysis is increasingly expected to support decision-making processes, such as maintenance planning, resource allocation, system design, and operational optimization. Integrating reliability modeling with decision support frameworks allows practitioners and decision-makers to move beyond reliability assessment toward actionable, reliability-informed decisions under uncertainty.

We invite submissions on topics related to reliability modeling and decision support for industrial engineering systems using Operations Research, industrial engineering, and data-driven methodologies. Contributions addressing both theoretical developments and practical applications are welcome.

We welcome the submission of original research papers that provide new insights and practical solutions to challenges in reliability modeling and decision support for industrial engineering systems. Submissions should demonstrate methodological rigor and clearly highlight their relevance to real-world industrial applications. Papers that illustrate how data-driven reliability analysis can enhance decision-making and system performance are particularly encouraged.

Potential topics

• Data-driven reliability modeling and parameter estimation using operational data
• Reliability analysis using statistical learning and machine learning methods
• Reliability models combining analytical formulations with data-driven learning approaches
• Decision support systems incorporating reliability analysis and risk information
• Optimization and decision-making problems informed by reliability performance
• Predictive maintenance and condition monitoring using data and learning models
• Multistate system and network reliability analysis
• Reliability evaluation of manufacturing, logistics, and supply chain systems
• Reliability modeling in energy, power, and infrastructure systems
• Stochastic modeling and uncertainty analysis in data-driven reliability studies
• Simulation, enumeration, and approximation methods for reliability evaluation
• System design and resource allocation considering reliability and operational data
• Reliability analysis supported by digital twins, data analytics, and intelligent systems
• Reliability, robustness, and resilience of industrial engineering systems

Timeline

• July 31, 2027: Submission deadline

Timeline

• December 31, 2026: Full paper submission deadline

Timeline

• September 30, 2026: Full paper submission deadline