Vahideh Bafandegan Emroozi | Maintenance | Industry Impact Academic Award

Dr. Vahideh Bafandegan Emroozi | Maintenance | Industry Impact Academic Award

Dr. Vahideh Bafandegan Emroozi, Ferdowsi university of Mashhad, Iran

Dr. Vahideh Bafandegan Emroozi is a highly accomplished industrial management researcher with a Ph.D. from Ferdowsi University of Mashhad. She has served as a Research Fellow at both Ferdowsi University and Sanabad University, contributing significantly to the fields of supply chain optimization, maintenance planning, and human error analysis. With expertise in mathematical modeling and IoT integration, her work addresses complex challenges in industrial systems. She has published extensively in top-tier journals, focusing on sustainability, reliability, and smart decision-making. Dr. Bafandegan Emroozi is known for her interdisciplinary approach and advanced skills in coding, simulation, and decision-making methodologies. πŸ“ŠπŸ”¬πŸ’‘

Publication Profile

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Google Scholar

πŸŽ“ Education

Dr. Bafandegan Emroozi earned her Ph.D. in Industrial Management (2019–2024) from Ferdowsi University of Mashhad, with a GPA of 19.49/20. Her dissertation focused on maintenance planning models using the Internet of Things (IoT) and human error considerations. She previously completed her M.Sc. in Industrial Management (2014–2017) from the same university with a GPA of 18.96/20. Her academic journey began with a B.Sc. in Industrial Engineering (2008–2012), also at Ferdowsi University, where she achieved a GPA of 15.23/20. Her education reflects a strong foundation in both management and engineering disciplines, reinforced by a deep understanding of system dynamics and optimization. πŸŽ“πŸ“˜πŸ“ˆ

πŸ’Ό Experience

Dr. Bafandegan Emroozi has held research positions at Sanabad University (2023–2024) and Ferdowsi University of Mashhad (2021–2023), where she advanced projects in maintenance optimization, supply chain management, and human reliability modeling. She has also lectured in courses such as Operations Research, Multi-Criteria Decision Making, and Strategic Management. Her research integrates advanced modeling techniques using tools like Python, MATLAB, GAMS, Vensim, and Lingo. Through her academic roles, she has contributed to national and international collaborations, case studies, and interdisciplinary projects targeting industrial sustainability, IoT applications, and smart supply chain innovations. πŸ§ͺπŸ–₯οΈπŸ“š

πŸ† Awards and Honors

Dr. Bafandegan Emroozi has received widespread academic recognition for her innovative contributions to industrial management and decision science. Her scholarly work has led to numerous publications in prestigious journals like Process Integration and Optimization for Sustainability and Quality and Reliability Engineering International. She has been invited to revise and publish impactful papers on human error modeling and maintenance efficiency. Her doctoral thesis earned top academic distinction, and she is frequently cited for her research on IoT-enabled maintenance and cognitive reliability. Her dedication to applied research and cross-disciplinary innovation has made her a notable contributor to Iran’s academic excellence. πŸ…πŸ“–πŸŒŸ

πŸ”¬ Research Focus

Dr. Bafandegan Emroozi’s research centers on supply chain optimization, human error modeling, and preventive maintenance planning. She leverages Internet of Things (IoT) technologies to enhance decision-making in industrial operations. Her work incorporates methods such as system dynamics, machine learning, mathematical modeling, and multi-criteria decision-making. She is also active in the study of cognitive reliability, vendor-managed inventory, and blockchain applications in supply chain systems. Her holistic approach addresses operational efficiency, sustainability, and risk management, making significant contributions to smart industry transformation and reliability engineering. βš™οΈπŸ“¦πŸ“‰πŸ’»

Publication Top Notes

πŸ“¦ A new supply chain design to solve supplier selection based on IoT and delivery reliability – cited by 39 (2023)
πŸ‘₯ A vendor-managed inventory model based on optimal retailers selection and reliability – cited by 32 (2023)
πŸ“ˆ A new model to design the suppliers portfolio in newsvendor problem based on product reliability – cited by 25 (2023)
🌱 Selecting green suppliers by considering IoT and CMCDM approach – cited by 19 (2023)
🧠 A new approach to human error assessment in financial services using modified CREAM and DANP – cited by 19 (2023)
πŸ› οΈ Improving industrial maintenance efficiency: Integrated production & human error optimization – cited by 18 (2024)
πŸ›’ Newsvendor model based on suppliers’ competence & group decision-making – cited by 18 (2022)
πŸ€– Optimizing human reliability via CREAM and group decision-making – cited by 16 (2024)
πŸš‡ MCDM in probabilistic environment for supplier selection (Urban Railway, Iran) – cited by 14 (2023)
πŸ”§ Improving supply chain quality & cost with VIKOR and optimization – cited by 13 (2024)

Qing Xia | Reliability | Best Academic Researcher Award

Dr. Qing Xia | Reliability | Best Academic Researcher Award

Dr. Qing Xia, Dalian Jiaotong University, China

Dr. Qing Xia is a dedicated Ph.D. scholar in Mechanical Engineering at Dalian Jiaotong University, with a specialized focus on reliability engineering and RAMS (Reliability, Availability, Maintainability, and Safety) for railway vehicles. As the principal investigator for national, provincial, and enterprise-funded projects, she has published 11 papers and filed multiple patents. Dr. Xia collaborates with CSR Tangshan Locomotive & Rolling Stock Co., Ltd., applying advanced analytical models in practical railway systems. She is a recipient of the prestigious National Scholarship for Doctoral Students in 2024 and a member of the Chinese Mechanical Engineering Society. Her research blends theory with impactful engineering solutions. πŸ›€οΈπŸ“˜πŸ”§

Publication Profile

Orcid

πŸŽ“ Education

Dr. Qing Xia is currently pursuing her Ph.D. in Mechanical Engineering at Dalian Jiaotong University, China, where she focuses on the development and analysis of high-reliability systems in the context of railway engineering. Her academic path is rooted in mechanical systems and reliability analysis, where she integrates modern analytical tools like fuzzy fault trees and Bayesian networks into railway vehicle design. Her education reflects a strong foundation in theoretical and applied mechanical engineering, especially in modeling failure prediction and improving maintainability. Her studies contribute directly to system safety, with a growing academic and industrial impact. πŸŽ“πŸ“πŸ“Š

πŸ’Ό Experience

Dr. Qing Xia has led one National Natural Science Foundation project, two provincial projects, and three enterprise-level projects, gaining extensive experience in research and real-world applications. As a key contributor to CSR Tangshan Locomotive & Rolling Stock Co., Ltd., she oversaw RAMS design and analysis of high-speed locomotives. Her work involves evaluating failure modes, repairability, and functional performance of railway subsystems. She has authored 11 academic publications, including SCI and EI-indexed papers, and filed three patents and one software copyright. Dr. Xia’s practical engagement with industry and academic research has strengthened her expertise in reliability engineering and mechanical system optimization. πŸš„πŸ”¬πŸ“ˆ

πŸ† Awards and Honors

Dr. Qing Xia was honored with the National Scholarship for Doctoral Students in 2024, recognizing her outstanding academic achievements and contributions to research. She has successfully filed three patents and one software copyright, reflecting innovation in reliability engineering. Her published works, including SCI and EI-indexed papers, further establish her as an emerging scholar in mechanical and railway engineering. As principal investigator in several high-profile projects, she has received commendations for applying theory into practice in collaboration with major industrial players. Her research excellence has been acknowledged both in academia and by enterprise partners. πŸŽ–οΈπŸ“‘πŸ’‘

πŸ” Research Focus

Dr. Qing Xia’s research centers on reliability engineering and RAMS for railway vehicles, particularly in complex systems where data is scarce or uncertain. She has developed hybrid models such as T-S fuzzy fault trees, HE-BN, FDFT, and IOWA-FCM to enhance risk analysis, failure prediction, and maintenance strategies. Her contributions also include OWA-based FMECA and fuzzy logic-driven diagnostics to improve system-level decision-making. Through her collaborative work with industry leaders, she applies these models in real-world railway subsystems, advancing the design and performance of high-speed locomotives. Her work bridges theoretical modeling with applied engineering in transportation systems. πŸ§ πŸš†βš™οΈ

Publication Top Notes

  • A System Safety Assessment Method Considering Risk Correlation
  • Robust optimization of EMU brake module based on interval analysis