This special issue seeks contributions on reliability modeling and decision support for industrial engineering systems, including manufacturing, supply chains, logistics, energy systems, transportation, and service operations. While traditional probabilistic and stochastic approaches remain important, advances in sensor technologies and data collection have enabled new data-driven approaches that combine machine learning and statistical methods with classical reliability theory.
The special issue welcomes both theoretical developments and practical applications that demonstrate how data-driven reliability analysis can enhance decision-making in maintenance planning, resource allocation, system design, and operational optimization. Papers should show methodological rigor and clear relevance to real-world industrial challenges, illustrating how reliability-informed approaches improve system performance.