Submission Deadline: 31 October 2026 View: 1406 Submit to Special Issue
Prof. Dr. Yi-Kuei Lin
Email: yklin@nycu.edu.tw
Affiliation: Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, 300093, Hsinchu, Taiwan
Research Interests: performance evaluation, stochastic network reliability, operations research, telecommunication management
Prof. Dr. Xufeng Zhao
Email: zx.peak@outlook.com
Affiliation: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210000, China
Research Interests: probability theory, stochastic process, reliability and maintenance theory, applications in computer and industrial systems
Prof. Dr. Cheng-Fu Huang
Email: cfuhuang@fcu.edu.tw
Affiliation: Department of Business Administration, Feng Chia University, Taichung, Taiwan
Research Interests: system reliability, network analysis, process capability analysis, performance assessment, six sigma quality
In modern industrial engineering systems, uncertainty is an inherent characteristic stemming from complex operational environments, variable demand, component aging, and external disruptions. To ensure system reliability, resilience, and cost-effectiveness, it is vital to develop and apply advanced methods capable of modeling stochastic behavior and supporting robust decision-making processes. The advanced methods could encompass the utilization of mathematical models and algorithms to optimize network design, risk analysis, and decision-making under uncertainty.
We invite original research papers on topics related to stochastic modeling of industrial engineering systems, including manufacturing systems, supply chain, power systems, and computer systems. We encourage submissions focusing on advanced methods for designing, planning, and optimizing industrial engineering systems while considering the stochastic factor. Topics of interest include, but are not limited to:
· Stochastic modeling or computer simulation for industrial engineering systems
· Reliability assessment of complex and multistate systems
· Algorithmic approaches for reliability enhancement and system optimization
· Case studies on reliability assessment in industrial engineering systems
· Redundancy allocation and maintenance optimization in industrial engineering systems
· Risk assessment and uncertainty quantification in industrial engineering systems
· Bayesian methods and machine learning for reliability modeling and prediction
· Decision support tools for stochastic reliability assessment in industrial engineering systems
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