Ph.D. Candidate at Kharazmi Universtiy in Iran.
Vahid Yahyapour Ganji is a supply chain researcher and analyst with a deep-rooted expertise in data-driven decision-making, mathematical optimization, and logistics systems. With a career bridging academia and industry, he has contributed to several high-impact studies on supply chain resilience, sustainability, and robust network design. Currently a Supply Chain Business Analyst at Farapokht, Tehran, he supports strategic procurement and risk analysis using advanced modeling tools. He is the co-author of multiple journal articles focused on optimization under uncertainty, vehicle routing, and digital transformation in logistics. His recent work emphasizes circular supply chains and integrates machine learning principles for performance evaluation. Vahid’s pragmatic background in LTL logistics, production planning, and systems analytics enhances his ability to approach research with operational insight. His analytical thinking and interdisciplinary skill set position him at the forefront of real-world machine learning applications in industrial systems.
Professional Profiles
Google Scholar | ORCID
Strengths for the Award
Vahid Yahyapour Ganji demonstrates a strong and evolving research trajectory in the fields of supply chain engineering, optimization, and logistics. His work, especially the recent publication on robust and data-driven circular supply chain networks, showcases a sophisticated understanding of resilience and responsiveness—key pillars in modern supply chain design. This research is particularly relevant in today’s dynamic socio-economic context where adaptability and sustainability are critical.
He has consistently engaged with high-impact problems through advanced mathematical modeling, optimization under uncertainty, and multi-objective frameworks. His publication record spans reputable journals and includes topics such as sustainable vehicle routing, hierarchical hub location problems, and digital resilience frameworks, indicating breadth as well as depth in his domain.
Moreover, his academic background is fortified by a top-ranked Master’s degree, where he was awarded a full scholarship and ranked third among graduates. He complements this academic excellence with a diverse set of practical experiences in project planning, supply chain supervision, and business analytics—contributing to the real-world relevance of his research. His technical proficiency in tools such as Python, GAMS, Power BI, and AnyLogistix further underlines his readiness to tackle data-intensive, complex modeling tasks.
Education Summary
Vahid Yahyapour Ganji earned his Master of Science in Logistics and Supply Chain Engineering from Kharazmi University (Tehran), where he graduated with distinction. His thesis focused on multi-objective mathematical modeling for hierarchical hub locations under congestion and uncertainty—a theme consistent throughout his later work. His coursework emphasized simulation, optimization, and multi-criteria decision-making using fuzzy logic and probabilistic tools. Prior to his master’s degree, he completed a Bachelor’s in Industrial Engineering at Iran University of Science and Technology, with a thesis focused on stock index prediction using artificial neural networks. This early interest in machine learning laid the groundwork for his future data-driven research. His academic foundation is further enriched by practical knowledge in transportation systems, logistics design, and applied operations research, positioning him as a data-literate problem-solver capable of advancing industrial applications through innovative algorithmic approaches.
Professional Experience
Vahid Yahyapour Ganji’s professional journey showcases a progression through multiple strategic and analytical roles across Iran’s industrial sector. He began as a Project Planning Engineer at Omran Sazan Mahab, managing EPS infrastructure timelines and resource allocation. At Tipax, he played a pioneering role in Iran’s first Less Than Truckload (LTL) service, overseeing pricing and last-mile logistics. He then served as Production Planning Supervisor at Nouyan Negin Parsian, where he led forecasting and BPMN-driven process improvements. As Product Manager and Sales Planning Manager at Tejarat Gostar Arisa, he shaped B2B product portfolios and built performance dashboards to streamline operations. Presently, at Farapokht, he drives supply chain analytics, trend forecasting, and vendor evaluation. Across these roles, he integrates business intelligence tools, such as Power BI and simulation platforms, with domain expertise—bridging data science and operations to deliver strategic outcomes.
Research Focus
Vahid Yahyapour Ganji’s research lies at the intersection of machine learning, supply chain optimization, and decision-making under uncertainty. His central focus is on developing robust, data-driven models that enhance supply chain resilience, circularity, and responsiveness. He employs advanced operations research techniques—such as multi-objective programming, stochastic modeling, and fuzzy systems—to address real-world logistics challenges. Vahid is particularly invested in integrating machine learning algorithms to optimize performance evaluations, sustainability metrics, and network structures. His work spans topics such as sustainable vehicle routing under variable traffic, hub location modeling with congestion, and DLARG (Digital, Lean, Agile, Resilient, Green) frameworks for energy systems. His methodological contributions emphasize adaptability, scalability, and real-time responsiveness—vital qualities for modern logistics systems in uncertain environments. His deep understanding of non-convex optimization and simulation tools empowers him to craft innovative, machine-learning-enabled solutions for global supply chain challenges.
Award and Honor
Vahid Yahyapour Ganji has been consistently recognized for his academic and analytical excellence. During his Master’s studies at Kharazmi University, he ranked third among his cohort and was awarded a full three-year academic scholarship in recognition of his academic performance and research potential. His leadership in multiple industry-academic research projects has also been acknowledged through co-authorships with senior researchers and repeated invitations to collaborate on optimization-centric publications. His participation in national conferences on entrepreneurship and business management adds to his scholarly contributions. Furthermore, his academic track record—coupled with his interdisciplinary research output—demonstrates not only individual achievement but also a commitment to solving large-scale, practical challenges in logistics and operations. These distinctions, along with his growing presence in peer-reviewed publications, make him a noteworthy candidate for recognition in machine learning application research.
Publication Top Notes
Title: A robust design of a circular supply chain network based on the resilience and responsiveness dimensions: A data-driven model
Authors: Vahid Yahyapour Ganji, Ehsan Hozan, Parisa Babolhavaeji, AmirReza Tajally, Mohssen GhanavatiNejad
Journal: Socio-Economic Planning Sciences, July 2025
Summary:
This article proposes a robust framework for designing circular supply chain networks by incorporating resilience and responsiveness as dual performance dimensions. The model employs a data-driven optimization approach that integrates real-time variability, uncertainty, and recovery capabilities, using machine learning-inspired data structuring. The authors provide case-based validation demonstrating how the model enhances network agility and sustainability.
Conclusion
Overall, Vahid Yahyapour Ganji presents a highly promising profile for the Best Researcher Award. His ability to combine theoretical rigor with practical insight into sustainable supply chain systems is a significant asset. His recent work on resilient and responsive circular supply chains addresses a critical global challenge and reflects a mature, impactful research direction. With further development of his publication portfolio and broader academic engagement, he stands out as a strong candidate deserving of recognition for his research contributions.
