Faculty Member at Iran University of Science and Technology, Iran
Dr. Masoud Yaghini is a distinguished faculty member in the Department of Rail Transportation at the Iran University of Science and Technology. Born on December 8, 1966, he holds an extensive academic and professional background in rail transportation planning and optimization techniques. With over two decades of experience, Dr. Yaghini has made substantial contributions to the fields of transportation logistics, network design, and data mining, particularly within the railway industry. His innovative approaches to complex rail transportation problems have earned him a reputation as a leading researcher in the field. Dr. Yaghini is widely published and continues to shape the future of transportation with cutting-edge research.
Professional Profile
Education
Dr. Yaghini received his Ph.D. in Rail Transportation Planning and Engineering from Northern Jiaotong University, Beijing, China, in 2003, with a focus on dynamic service network design. He also holds an MSc and BSc in Industrial Management from Islamic Azad University, Tehran. His master’s thesis on resource assignment optimization in preventive maintenance laid the foundation for his interest in large-scale optimization problems. Additionally, he furthered his knowledge with specialized training in Ergonomics and Human Factors for Railways from the University of Birmingham, UK, in 2005. This diverse educational background has equipped Dr. Yaghini with both theoretical and practical expertise in optimizing transportation systems.
Professional Experience
Dr. Yaghini has over 20 years of professional experience, primarily as a faculty member at the Iran University of Science and Technology. He teaches a wide range of courses, from advanced computer programming to railway operations management and data mining in transportation. His professional experience extends beyond academia into consultancy work in optimization and transportation planning. Dr. Yaghini has also conducted numerous short courses and workshops in data mining, information management, and metaheuristic algorithms for both academic institutions and private companies. His role as an educator and consultant has allowed him to bridge the gap between academic research and real-world transportation challenges.
Research Interests
Dr. Yaghini’s research primarily focuses on optimization problems in rail transportation, including train scheduling, fleet sizing, and locomotive scheduling. He has a strong interest in metaheuristics such as Genetic Algorithms, Tabu Search, and Ant Colony Optimization, as well as exact solution methods like Column Generation and Branch and Cut. His work also explores data mining techniques applied to railway systems, such as the prediction of train delays and analysis of accident data. His research is driven by the need to optimize and improve efficiency in transportation systems, particularly in large-scale rail networks. His work has significant practical implications for enhancing railway operations and minimizing costs.
Awards and Honors
Dr. Yaghini’s contributions to transportation research have earned him multiple accolades, though his recognition mainly stems from his published works in high-impact journals such as Applied Mathematical Modelling and Journal of Transportation Engineering. He has been recognized for his work on solving complex railway optimization problems through innovative algorithms like Ant Colony Optimization and Simulated Annealing. In addition to his publications, Dr. Yaghini has been invited to present his findings at numerous international conferences. While he has not widely publicized any specific awards, his ongoing research contributions have earned him a solid reputation in the global transportation research community, marking him as a key figure in rail transportation planning and optimization.
Conclusion
Dr. Masoud Yaghini’s research portfolio is impressive, with a strong emphasis on rail transportation and optimization problems. His consistent contributions to both academic knowledge and practical railway systems demonstrate his potential for recognition as a top researcher. By broadening his collaborative network and impact beyond academia, he could further strengthen his candidacy for prestigious awards like the Best Researcher Award.
Publication top noted
- Online prediction of arrival and departure times in each station for passenger trains using machine learning methods
- Vafaei, S., Yaghini, M.
- Transportation Engineering, 2024
- 📖 0 citations
- Analysis of the relationship between geometric parameters of railway track and twist failure by using data mining techniques
- Izadi Yazdan Abadi, E., Khadem Sameni, M., Yaghini, M.
- Engineering Failure Analysis, 2023
- 📖 2 citations
- A mathematical formulation and an LP-based neighborhood search matheuristic solution method for the integrated train blocking and shipment path problem
- Yaghini, M., Mirghavami, M., Zare Andaryan, A.
- Networks, 2021
- 📖 5 citations
- Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times
- Zarook, Y., Rezaeian, J., Mahdavi, I., Yaghini, M.
- RAIRO – Operations Research, 2021
- 📖 10 citations
- An adaptive structure on a new local branching algorithm using instantaneous dimensions and convergence speed: a case study for multi-commodity network design problems
- Hajiyan, H., Yaghini, M.
- SN Applied Sciences, 2020
- 📖 1 citation
- Optimization of embedded rail slab track with respect to environmental vibrations
- Esmaeili, M., Yaghini, M., Moslemipour, S.
- Scientia Iranica, 2020
- 📖 0 citations
- An Effective Improvement to Main Non-periodic Train Scheduling Models by a New Headway Definition
- Jafarian-Moghaddam, A.R., Yaghini, M.
- Iranian Journal of Science and Technology – Transactions of Civil Engineering, 2019
- 📖 2 citations
- Optimizing headways for urban rail transit services using adaptive particle swarm algorithms
- Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
- Public Transport, 2018
- 📖 26 citations
- Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach
- Hassannayebi, E., Zegordi, S.H., Amin-Naseri, M.R., Yaghini, M.
- Operational Research, 2017
- 📖 48 citations
- Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals
- Hassannayebi, E., Zegordi, S.H., Yaghini, M., Amin-Naseri, M.R.
- Transportation Planning and Technology, 2017
- 📖 35 citations
