Mehri Bagherian | Robust Optimization | Women Researcher Award

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Women Researcher Award

Mehri Bagherian
University of Guilan
Mehri Bagherian
Affiliation University of Guilan
Country Iran
Scopus ID 36105020600
Documents 18
Citations 84
h-index 5
Subject Area Robust Optimization
Event International Research Scientist Awards
ORCID 0000-0002-6870-8502

Mehri Bagherian is associated with the University of Guilan and has contributed to the field of robust optimization and decision sciences through scholarly publications and applied analytical research. Her academic profile demonstrates interdisciplinary engagement in optimization modeling, operational research, and mathematical decision-making methods. The recognition under the International Research Scientist Awards highlights her ongoing contributions to academic research and scientific development.[1]

Abstract

This article presents an overview of the academic profile and research activities of Mehri Bagherian in the area of robust optimization and operations research. Her scholarly work focuses on analytical optimization models, uncertainty management, and strategic decision-making methodologies relevant to engineering and management sciences. The article also evaluates her research influence, publication record, and suitability for recognition through the Women Researcher Award under the International Research Scientist Awards program. Her academic contributions reflect continued engagement in quantitative research and interdisciplinary scientific development.[1][2]

Keywords

Robust Optimization, Operations Research, Decision Sciences, Mathematical Modeling, Optimization Theory, Scientific Research, Women Researcher Award, Quantitative Analysis, Engineering Management, Research Recognition.

Introduction

Robust optimization has become an important research domain for addressing uncertainty and improving decision-making reliability in complex systems. Researchers working in this field contribute to optimization methodologies that support industrial planning,[1] Academic recognition programs such as the Women Researcher Award acknowledge researchers whose work contributes to scientific advancement and interdisciplinary innovation. [4]

Research Profile

Mehri Bagherian is affiliated with the University of Guilan in Iran and is academically associated with optimization-focused research areas. Her Scopus author profile indicates scholarly contributions in robust optimization and related quantitative methodologies. Her publication record demonstrates engagement in applied mathematical and operational research topics relevant to modern industrial systems.[1]

Research Contributions

The research contributions of Mehri Bagherian primarily involve optimization techniques designed to improve system reliability and strategic planning under uncertainty. Her work aligns with modern developments in operational analysis and robust decision frameworks used in engineering and management sciences.[2]

  • Development of optimization-based analytical models for uncertain environments.
  • Research on operational efficiency and mathematical decision-support systems.
  • Application of quantitative methods in engineering and industrial planning.
  • Contribution to interdisciplinary optimization and management studies.

Publications

The publication topics associated with Mehri Bagherian primarily focus on robust optimization, operational research, mathematical modeling, and decision-making under uncertainty. Her studies examine optimization strategies for improving efficiency, reliability, and analytical performance in industrial and engineering systems. The research contributes to quantitative problem-solving methodologies applicable to management sciences, logistics, and strategic operational planning.[1]

Research Impact

The research impact of Mehri Bagherian can be observed through indexed citations and continued publication activity in optimization-related domains. Citation-based indicators provide evidence of scholarly visibility and demonstrate that her work contributes to ongoing academic discussions within operational research and quantitative decision-making fields.[3] Such research areas remain relevant across engineering, industrial management, and applied mathematical sciences.[2]

Award Suitability

The Women Researcher Award recognizes individuals demonstrating sustained academic engagement, research productivity, and scholarly contribution within their respective disciplines. Mehri Bagherian’s documented publication activity, citation metrics, and specialization in robust optimization align with the evaluation criteria commonly associated with international academic recognition programs.[4]

Conclusion

Mehri Bagherian has established an academic profile centered on robust optimization and quantitative analytical research. Her indexed publications, citation record, and research engagement indicate meaningful participation in operational research and decision sciences. The Women Researcher Award article highlights her scholarly contributions and reflects the broader significance of interdisciplinary optimization research in contemporary academic environments.[1][4]

References

    1. Elsevier. (n.d.). Scopus author details: Mehri Bagherian, Author ID 36105020600. Scopus.
      https://www.scopus.com/authid/detail.uri?authorId=36105020600
    2. Bagherian, M. (2018). Unmanned aerial vehicle terrain following/terrain avoidance/threat avoidance trajectory planning using fuzzy logic. 
      https://www.researchgate.net/publication/323978826_Unmanned_Aerial_Vehicle_Terrain_FollowingTerrain_AvoidanceThreat_Avoidance_trajectory_planning_using_fuzzy_logic
    3. ORCID. (n.d.). Mehri Bagherian ORCID profile.
      https://orcid.org/0000-0002-6870-8502
    4. Zardashti, R., & Bagherian, M. (2009). A new model for optimal TF/TA flight path design problem.
      https://www.semanticscholar.org/paper/A-new-model-for-optimal-TF-TA-flight-path-design-Zardashti-Bagherian/40ac87c7d8726c7ef219048b6ae67c3a5385c535

Jingsheng Feng | Decision Sciences | Best Paper Award

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Mr. Jingsheng Feng | Decision Sciences | Best Paper Award

Research Assistant | Hefei University of Technology | China

Dr. Jingsheng Feng, a distinguished researcher at Hefei University of Technology, China, focuses on advanced logistics network optimization, supply chain resilience, and intelligent decision-making systems. His research integrates mathematical modeling, fuzzy logic, and multi-objective optimization to tackle complex challenges in customized logistics and industrial engineering. Notably, his work published in Computers & Industrial Engineering presents a reliable logistics network design model incorporating demand sensitivity to correlated disruptions, enhancing system robustness under uncertainty. In the International Journal of Systems Science: Operations & Logistics (, he co-developed disruption response strategy models for supplier selection and order allocation to support firms in maintaining operational stability during disruptions. His study in Expert Systems with Applications proposed fuzzy multi-objective team decision models for customer order decoupling point (CODP) and supplier selection, facilitating collaborative and data-driven decision-making in customized logistics supply chains. Additionally, his  work in Computers & Industrial Engineering explored battery swapping service network deployment, addressing behavioral factors such as driver range anxiety and impatience. Through his interdisciplinary contributions, Dr. Feng bridges theory and practice in logistics and supply chain engineering, promoting demand responsiveness, risk mitigation, and intelligent system design to advance sustainable, adaptive, and human-centered logistics strategies for modern industrial ecosystems. Her research impact is evident from 15 citations across 4 documents with an h-index of 2.

Profiles : ORCID | Scopus 

 

Featured Publications


1 .Feng, J., Hu, X., Xu, L., Luo, S., & Chen, J. (2025). Reliable logistics network design joint optimization problem applying demand sensitivity to correlated disruptions. Computers & Industrial Engineering.

2. Xu, L., Hu, X., Wu, Z., Luo, S., Feng, J., & Zhang, X. (2025). Disruption response strategy models for supplier selection and order allocation in customised logistics service supply chain. International Journal of Systems Science: Operations & Logistics.

3. Xu, L., Hu, X., Zhang, Y., Feng, J., & Luo, S. (2024). A fuzzy multiobjective team decision model for CODP and supplier selection in customized logistics service supply chain. Expert Systems with Applications, 213, 121387.

4. Hu, X., Zhang, X., Xu, L., Feng, J., & Luo, S. (2024). The battery swapping service network deployment problem: Impact of driver range anxiety and impatience. Computers & Industrial Engineering, 172, 110189.

Jafar Fathali | Operations Research | Best Researcher Award

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Prof.Jafar Fathali | Operations Research | Best Researcher Award

University Professor at Shahrood University of Technology, Iran 

Professor Jafar Fathali 🎓 is a renowned academic in Operations Research and Applied Mathematics, currently serving as a Professor at the Faculty of Mathematical Sciences, Shahrood University of Technology, Iran 🇮🇷. With decades of contribution to location theory, heuristic optimization, and scheduling problems , he has become a distinguished figure in computational mathematics. A prolific researcher, Prof. Fathali has authored over 50+ peer-reviewed journal articles  in internationally recognized platforms such as EJOR, Soft Computing, and Computers & Industrial Engineering. He is actively involved in scholarly communities including the Iranian Mathematical Society and the Iranian Operations Research Society . Beyond research, he contributes as a referee for leading journals, mentoring students and advancing mathematical modeling in real-world applications. His academic journey is defined by innovation, persistence, and leadership , making him a vital contributor to the global research ecosystem .

🔹Professional Profile

SCOPUS

ORCID

📘 Education & Experience

Prof. Jafar Fathali holds a BSc in Applied Mathematics from Ferdowsi University of Mashhad , an MSc from Amirkabir University of Technology , and earned his Ph.D. in Applied Mathematics from Ferdowsi University in 2005 . With a solid foundation in mathematical theories, he began teaching at Shahrood University of Technology, where he advanced to a full professorship . Over the years, he has taught a wide array of undergraduate and graduate courses, including Operations Research, Advanced Linear & Nonlinear Programming, Combinatorial Optimization, and Numerical Analysis . His expertise spans both theoretical frameworks and practical applications, equipping students with problem-solving and analytical skills 🔍. With his academic and mentoring experience, Prof. Fathali has played a key role in shaping Iran’s next generation of mathematicians and operations research .

🚀 Professional Development

Professor Fathali has shown remarkable growth in academia through innovative research, interdisciplinary collaborations, and active journal reviewing . He has reviewed articles for top-tier journals such as European Journal of Operational Research, Transportation Research Part E, Soft Computing, and Optimization Methods and Software . He is a member of the Iranian Mathematical Society, Iranian Operations Research Society, and Iranian Statistics Society , reflecting his deep involvement in the academic community. His ability to integrate fuzzy logic, graph theory, and metaheuristic algorithms into practical models has enhanced decision-making strategies across industries . Prof. Fathali has also co-developed numerous hybrid algorithms involving genetic algorithms, ant colony optimization, and variable neighborhood search for solving complex problems . His active mentorship, editorial contributions, and research collaborations are key indicators of a career deeply committed to academic excellence, growth, and innovation .

🔬 Research Focus

Professor Jafar Fathali’s research is firmly rooted in Operations Research, with an emphasis on location theory , combinatorial optimization, and scheduling problems . He specializes in designing algorithms for complex decision-making models such as the p-median, p-center, and core location problems across graphs and trees . His methods employ heuristic techniques, metaheuristics (e.g., genetic algorithms , particle swarm optimization , and fuzzy logic  to model real-world uncertainties in logistics, network design, and resource allocation. Prof. Fathali has also explored inverse and semi-obnoxious location problems, expanding the scope of location models to account for service inefficiencies and backup facilities . His works address both theoretical and applied aspects, blending mathematical rigor with practical implementation . With continuous innovations in modeling and optimization, his contributions have significantly advanced the field of applied mathematics and operations research .

🏆 Awards & Honors

While specific awards and honors for Professor Jafar Fathali are not individually listed, his academic reputation is underscored by the impact and volume of his scholarly work . Having published in high-impact journals like European Journal of Operational Research and Soft Computing, his research has earned wide recognition and citation 🏆. Being a referee for over a dozen international journals and collaborating with well-known scholars such as R.E. Burkard, indicates peer acknowledgment and respect . His sustained publication record, editorial engagements, and frequent invitations to review complex mathematical models highlight his research excellence and international credibility . His contributions have helped define solutions for complex logistics and scheduling challenges, securing his place among Iran’s most influential operations research . With ongoing recognition from both academic institutions and scholarly circles, Prof. Fathali continues to be a role model for aspiring mathematicians and OR specialists globally .

🔹Publication of Top Notes

1.Convexity and sensitivity analysis of the median line location problem

Authors: Mehdi Golpayegani, Jafar Fathali
Year: 2025
Journal: International Journal of Systems Science: Operations & Logistics
DOI: 10.1080/23302674.2025.2529967

2.Greedy algorithms for the inverse center line location problem

Authors: Mehdi Golpayegani, Jafar Fathali
Year: 2025
Journal: Expert Systems with Applications
DOI: 10.1016/j.eswa.2025.129064

3.Fuzzy balanced allocation problem with efficiency on facilities

Authors: Azam Azodi, Jafar Fathali, Mojtaba Ghiyasi, Tahereh Sayar
Year: 2023
Journal: Soft Computing
DOI: 10.1007/s00500-022-07695-4

4.The balanced 2-median and 2-maxian problems on a tree

Authors: Jafar Fathali, Mehdi Zaferanieh
Year: 2023
Journal: Journal of Combinatorial Optimization
DOI: 10.1007/s10878-023-00997-9

5.Finding the absolute and vertex center of a fuzzy tree

Authors: Fatemeh Taleshian, Jafar Fathali, Nemat Allah Taghi-Nezhad
Year: 2022
Journal: Transportation Letters
DOI: 10.1080/19427867.2021.1909797

6.The minimum information approach to the uncapacitated p-median facility location problem

Authors: Mehdi Zaferanieh, Maryam Abareshi, Jafar Fathali
Year: 2022
Journal: Transportation Letters
DOI: 10.1080/19427867.2020.1864595

7.Fuzzy Balanced Allocation Problem with Efficiency on Servers

Authors: Azam Azodi, Jafar Fathali, Mojtaba Ghiyasi, Tahereh Sayar
Year: 2021
Type: Preprint
DOI: 10.21203/rs.3.rs-444116/v1

8.Inverse and reverse balanced facility location problems with variable edge lengths on trees

Authors: Shahede Omidi, Jafar Fathali, Morteza Nazari
Year: 2020
Journal: OPSEARCH
DOI: 10.1007/s12597-019-00428-6

9.Finding an optimal core on a tree network with M/G/c/c state-dependent queues

Authors: Mehrdad Moshtagh, Jafar Fathali, James MacGregor Smith, Nezam Mahdavi-Amiri
Year: 2019
Journal: Mathematical Methods of Operations Research
DOI: 10.1007/s00186-018-0651-3

10.The Stochastic Queue Core problem, evacuation networks, and state-dependent queues

Authors: Mehrdad Moshtagh, Jafar Fathali, J. MacGregor Smith
Year: 2018
 Journal: European Journal of Operational Research
 DOI: 10.1016/j.ejor.2018.02.026

🏁Conclusion

Professor Fathali’s research stands out due to its mathematical rigor, practical relevance, and algorithmic innovation. His work significantly advances the optimization and decision sciences field, contributing both theoretical frameworks and practical solutions. These qualities, combined with his sustained academic output, collaborative spirit, and international impact, make him an ideal candidate for the Best Researcher Award.

Ramin Ahadi | Operations Research | Best Researcher Award

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Mr.Ramin Ahadi | Operations Research | Best Researcher Award

Doctoral Candidate at University of Cologne , Germany

A final-year Ph.D. candidate  at the University of Cologne and IE Business School, this researcher specializes in practical operations management and data science. Their work focuses on developing intelligent decision support systems using agent-based simulation , machine learning , and deep reinforcement learning. With expertise in smart mobility , energy systems , and sustainability, they bridge real-world problems with cutting-edge technology. Fluent in Python and other tools, they actively teach ML to graduate students and collaborate across academia and industry. Passionate about climate solutions 🌱, they aim to innovate for a greener and smarter world.

Professional Profile

Google Scholar Profile

Education & Experience 

Holding a Ph.D. (2025 exp.) in Information Systems & Operations Management from the University of Cologne  and currently a visiting scholar at IE Business School, Madrid , they also earned M.Sc. and B.Sc. degrees in Iran in Industrial and Mechanical Engineering respectively. With roles as researcher, lecturer , and tutor, their journey spans Europe and Asia. They’ve worked on EU-level energy and mobility research projects, simulations for EV fleets , and optimization algorithms. Teaching advanced analytics, leading grants, and collaborating with cities like Berlin and Paris , they blend deep technical skills with real-world impact.

Professional Development 

This candidate continuously enhances their skill set through hands-on research, collaborative grant writing , and academic publishing . They lead cutting-edge projects using Python, TensorFlow, PyTorch, and simulation tools like Simpy and Pyomo . Their development includes teaching graduate-level machine learning courses , engaging in high-impact conferences like ICIS and AAMAS , and working with institutions like EWI. Industry collaborations include EV charging systems and sustainable logistics . Their commitment to sustainability, innovation, and smart city solutions  positions them as a future leader in technology-driven operations management.

Research Focus 

Their research centers on smart mobility , energy systems , and climate-conscious technologies 🌱. They design agent-based simulations and deep learning models  to manage shared autonomous fleets and EV charging. Key areas include dynamic fleet pricing, ride-hailing, digital twins of mobility networks , and predictive analytics for load scheduling. They bridge theory and application by leveraging real-world data from European cities . Using advanced optimization (GA, MPC, RL) and simulation, their work contributes to more sustainable urban ecosystems. Their core mission is to build data-driven, adaptive platforms for smarter, greener cities.

Awards & Honors 

Recognized for academic excellence and innovation , they ranked in the top 5% during their M.Sc. and top 10% in their B.Sc. programs . They earned a competitive research scholarship from the Institute of Energy Economics at the University of Cologne and co-led multiple successful EU research grant proposals . Their work has been presented at top-tier conferences like ICIS, ECIS, and WITS . They’ve also made an impact through teaching awards and invitations to speak on sustainability in mobility and energy systems . Their excellence extends to both academia and industry collaborations.

Publication Top Notes

1.Ahadi, R., Ketter, W., Collins, J., & Daina, N. (2023).
“Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets.”
Transportation Science, 57(3), 613–630.
 Summary: This study presents a cooperative learning framework for optimizing electric vehicle (EV) charging across shared autonomous vehicle fleets. The model integrates real-time learning with coordination strategies to improve efficiency, grid stability, and user satisfaction.

2.Khalilzadeh, M., Neghabi, H., & Ahadi, R. (2023).
“An Application of Approximate Dynamic Programming in Multi-Period Multi-Product Advertising Budgeting.”
Journal of Industrial & Management Optimization, 19(1).
Summary: This paper develops an approximate dynamic programming approach to optimize advertising budgets over time for multiple products. It accounts for intertemporal trade-offs and uncertain returns, showcasing the method’s superiority to static approaches.

3.Yazdi, L., Ahadi, R., & Rezaee, B. (2019).
“Optimal Electric Vehicle Charging Station Placing with Integration of Renewable Energy.”
15th Iran International Industrial Engineering Conference (IIIEC), 47–51.
Summary: This conference paper investigates optimal site selection for EV charging stations using a multi-objective model that includes renewable energy generation and urban demand forecasting.

Conclusion

R. Ahadi exemplifies the qualities of a future-leading scholar with impactful, sustainable, and innovative contributions to operations management and intelligent systems. Their work directly contributes to the global challenge of building greener, smarter urban ecosystems—making them highly deserving of the Best Researcher Award.

Dr. Vahideh Bafandegan Emroozi | Maintenance | Women Researcher Award

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Dr. Vahideh Bafandegan Emroozi | Maintenance | Women Researcher Award

Author , Ferdowsi university of Mashhad , Iran

Vahideh Bafandegan Emroozi is a passionate Iranian researcher specializing in industrial management and optimization. 🎓 With a Ph.D. from Ferdowsi University of Mashhad, her work bridges technology and human-centric approaches. 📊 Her research spans supply chain innovation, IoT applications, and human error analysis. 🤖🧠 She has published in esteemed journals and held research fellowships at Ferdowsi and Sanabad Universities. 📚✍️ Known for her analytical skills and academic dedication, Vahideh continues to contribute significantly to industrial systems and decision sciences. 🔍📈 Her collaborative spirit and teaching experience further highlight her dynamic role in academia. 👩‍🏫🌐

Professional Profile:

SCOPUS

Education & Experience:

Vahideh earned her Ph.D. in Industrial Management (2019–2024) 🎓 from Ferdowsi University, where her thesis focused on IoT-based maintenance and human error modeling. 📡🛠️ She also completed an M.Sc. in Industrial Management (2014–2017) with a high GPA of 18.96/20 📚 and a B.Sc. in Industrial Engineering (2008–2012). 🏗️ Her academic journey led to research fellow roles at Ferdowsi University (2021–2023) and Sanabad University (2023–2024). 🔬🏛️ In addition to research, she has taught Operations Research, Strategic Management, and Multi-Criteria Decision Making. 👩‍🏫 Her experience reflects a strong foundation in both theory and application. 💼🧮

Professional Development:

Vahideh continually builds her academic and technical skills through professional development. 📈💡 She has mastered analytical and modeling tools such as Python, MATLAB, GAMS, LINGO, LaTeX, and Vensim. 💻📐 Her commitment to research excellence is evident in her publications in Scopus-indexed journals 📄🔍 and her work on complex topics like green supply chain management and pandemic response strategies. 🌍📦 She actively contributes to knowledge dissemination through teaching, collaborative research, and methodological innovation. 📊🧠 Her engagement with multidisciplinary topics ensures she remains at the forefront of industrial and systems engineering. 🚀📘

Research Focus:

Vahideh’s research spans across multiple domains in industrial management. 🏭🔍 Her core interests include supply chain management, optimization, and maintenance planning. 🧾🛠️ She also explores the effects of human error, reliability analysis, and inventory control systems. ⚙️🧠📦 A significant part of her work integrates the Internet of Things (IoT) 🌐 with system dynamics and mathematical modeling 📊📉 to improve industrial decision-making. Her goal is to create smarter, more resilient, and sustainable industrial systems. 🌱💡 Her innovative contributions are driving progress in operational efficiency and risk reduction. 🚚📈

Awards & Honors:

While specific awards were not listed, Vahideh’s academic record speaks to her excellence. 🌟 She achieved outstanding GPAs in both her Ph.D. (19.49/20) and M.Sc. (18.96/20) programs. 🥇📘 Her research has been recognized with publications in high-impact international journals like Process Integration and Optimization for Sustainability and Journal of Industrial and Management Optimization. 📚✨ She has contributed novel methodologies in green supplier selection, VIKOR optimization, and system dynamics during COVID-19. 🧪🌐 Her roles as research fellow at top Iranian universities also reflect her academic merit and potential. 🏛️🔬

Publication Top Notes

1. Markov Chain-Based Model for IoT-Driven Maintenance Planning with Human Error and Spare Part Considerations

Authors: Bafandegan Emroozi, Vahideh; Doostparast, Mahdi
Journal: Reliability Engineering and System Safety
Year: 2025
Access: Open Access
Citations: 0 (as of now)

🔍 Summary:
This article introduces a novel Markov chain-based framework that integrates the Internet of Things (IoT) into industrial maintenance planning. The model accounts for human error probabilities and spare part availability, creating a dynamic and realistic approach to predictive maintenance. 📈 The use of Markov chains enables the system to model stochastic transitions between equipment states, improving decision-making accuracy. 🤖📦 The study enhances reliability and safety in industrial systems by aligning IoT data with probabilistic risk and resource planning, offering a scalable tool for real-time maintenance strategy optimization. 🛠️📊

2. Enhancing Industrial Maintenance Planning: Optimization of Human Error Reduction and Spare Parts Management

Authors: Bafandegan Emroozi, Vahideh; Kazemi, Mostafa; Doostparast, Mahdi
Journal: Operations Research Perspectives
Year: 2025
Access: Open Access
Citations: 0 (as of now)

🔍 Summary:
This paper proposes an optimization model aimed at improving maintenance planning by focusing on human error mitigation and efficient spare parts management. 👷⚙️ It applies advanced operations research techniques to identify cost-effective strategies for minimizing failures and delays due to incorrect human actions or resource shortages. The model bridges the gap between human factors engineering and logistical planning, integrating real-time data and decision analysis. 🧠📦 It offers a comprehensive framework suitable for modern industries aiming to balance cost, reliability, and safety. 🧾📉

Conclusion

Vahideh Bafandegan Emroozi exemplifies the qualities celebrated by Women in Research Awards: innovation, impact, leadership, and academic excellence. 🌟 Her work addresses critical industrial challenges through smart technologies and rigorous modeling, while her dedication to teaching and mentoring amplifies her influence. As a pioneering female researcher in a highly technical and traditionally male-dominated field, she is not only technically accomplished but also a role model for aspiring women in STEM. 🧠🔬👩‍🏫 She is highly deserving of recognition through a Women Researcher Award.