Best Paper Award
Ali Mohaghar
Professor, Department of Production and Operations Management, Faculty of Industrial and Technology Management, University of Tehran, Iran.
This article documents the academic recognition associated with the Best Paper Award attributed to research connected with Professor Ali Mohaghar of the University of Tehran. The award recognition highlights scholarly contributions in industrial engineering, decision sciences, supply chain management, system dynamics, technology management, and operations research. The recognized work, Advanced hyperparameter optimization and adaptive synthetic sampling in machine learning for predictive maintenance of industrial machinery, reflects ongoing research interests in analytical decision-making and industrial systems improvement.[1][3]
Abstract
Ali Mohaghar is an Iranian scholar whose research spans industrial engineering, strategic management, technology policy, supply chain management, systems thinking, decision sciences, and quantitative optimization. His academic record includes Scopus-indexed publications, extensive supervision and leadership activities, and scholarly collaborations across industry and academia. The Best Paper Award recognition is associated with a 2025 study on predictive maintenance that integrates machine learning, adaptive synthetic sampling, and hyperparameter optimization for industrial machinery reliability improvement.[1][3]
Keywords
Strategic Management; Supply Chain Management; Multi-Criteria Decision Making (MCDM); Inventory Control; System Dynamics; Industrial Engineering; Technology Management; Predictive Maintenance; Decision Sciences; Operations Research.
Introduction
Professor Ali Mohaghar has contributed to a broad range of management and engineering disciplines through research, teaching, academic administration, and professional service. His work combines theoretical and applied methodologies including system dynamics, optimization, fuzzy decision-making, data envelopment analysis, technology innovation studies, and supply chain analytics. These activities have supported the development of research frameworks addressing industrial competitiveness, organizational performance, and technological transformation.[1][2]
Research Profile
Ali Mohaghar earned a Ph.D. in Industrial Engineering from Tarbiat Modares University in 2001 with a specialization in maintenance systems and operations research. He previously obtained an M.S. in Industrial Engineering from Isfahan University of Technology and a B.S. in Industrial Engineering from Sharif University of Technology. His professional appointments have included university leadership, advisory positions, editorial board service, policy development activities, and participation in technology commercialization initiatives.
- Professor, University of Tehran.
- Editorial and scientific committee memberships.
- Research leadership in technology management and operations research.
- Contributions to commercialization, innovation, and organizational excellence studies.
Research Contributions
The research portfolio of Ali Mohaghar demonstrates sustained engagement with supply chain management, strategic decision-making, organizational excellence, technology innovation ecosystems, knowledge management, Industry 4.0 readiness, project management, logistics optimization, and socio-technical systems modeling. His publications frequently employ system dynamics, grounded theory, fuzzy methods, multi-criteria decision-making approaches, and simulation-based analyses to address real-world managerial challenges.[1]
A notable dimension of his work involves integrating analytical models with practical applications in manufacturing, transportation, energy systems, public administration, healthcare, and technology-based enterprises. These interdisciplinary contributions have helped bridge academic theory and operational practice across multiple sectors.[2]
Publications
Among Professor Ali Mohaghar’s highly cited and academically significant publications are studies addressing supplier selection, decision support systems, knowledge management strategy evaluation, predictive maintenance, supply chain optimization, and technology management. The following publications are particularly relevant to the award recognition and scholarly impact profile.
Additional publications cover Industry 4.0 implementation, IoT adoption, project supply chain management, technology catch-up models, entrepreneurial ecosystems, organizational excellence methodologies, reverse logistics, predictive analytics, and system dynamics applications. These works collectively demonstrate a long-term commitment to evidence-based decision support and industrial systems improvement.[1]
Research Impact
According to available citation databases, Professor Ali Mohaghar has accumulated substantial scholarly influence. Scopus records indicate 37 indexed documents, 386 citations, and an h-index of 8, while Google Scholar reports broader citation coverage exceeding 1,700 citations and an h-index of 19.[1][2]
His research has been cited across fields including industrial engineering, management science, operations research, technology policy, supply chain analytics, and strategic management. The breadth of these citations indicates interdisciplinary relevance and continuing scholarly engagement.
Award Suitability
The Best Paper Award recognition aligns with Professor Ali Mohaghar’s established record of methodological innovation and applied research. The awarded study on predictive maintenance integrates machine learning optimization, adaptive synthetic sampling, and industrial reliability analysis, representing a contemporary contribution to intelligent maintenance systems and industrial analytics.[3]
The paper’s focus on predictive maintenance is consistent with the author’s long-standing research interests in systems engineering, operations management, and decision support methodologies. Its practical relevance to industrial machinery performance and maintenance planning supports its suitability for scholarly recognition and award consideration.[3]
Conclusion
Ali Mohaghar has established a significant academic profile through research, teaching, and professional leadership in industrial engineering and management sciences. His publication record demonstrates contributions to decision sciences, supply chain management, technology innovation, and organizational systems analysis. The Best Paper Award associated with the 2025 predictive maintenance study represents a continuation of his scholarly engagement with contemporary industrial challenges and data-driven decision-making methodologies.[1][3]
External Links
References
- Elsevier. (n.d.). Scopus author details: Ali Mohaghar, Author ID 8533788100. Scopus. https://www.scopus.com/authid/detail.uri?authorId=8533788100
- Google Scholar. (n.d.). Ali Mohaghar citation profile. https://scholar.google.com/citations?user=RRwlxLgAAAAJ&hl=en&oi=sra
- Khani, A. M., Mohaghar, A., Rezasoltani, A., & Hosseinian, S. H. (2025). Advanced hyperparameter optimization and adaptive synthetic sampling in machine learning for predictive maintenance of industrial machinery. International Journal of Research in Industrial Engineering. DOI: https://doi.org/10.22105/riej.2025.500994.1528
- Sanayei, A., Mousavi, S. F., Abdi, M. R., & Mohaghar, A. (2008). An integrated group decision-making process for supplier selection and order allocation using multi-attribute utility theory and linear programming. Journal of the Franklin Institute, 345(7), 731–747. DOI: https://doi.org/10.1016/j.jfranklin.2008.03.005
- Sarraf, A. Z., Mohaghar, A., & Bazargani, H. (2013). Developing TOPSIS method using statistical normalization for selecting knowledge management strategies. Journal of Industrial Engineering and Management. DOI: https://hdl.handle.net/2099/14135