Ph.D. student/Junior researcher, Lappeenranta University of Technology, Finland
Danyal Aghajani is a dedicated Ph.D. student at Lappeenranta University of Technology with a profound interest in optimization, uncertainty programming, and supply chain modeling. He holds a Master’s degree in Industrial Engineering from Iran University of Science and Technology and a Bachelor’s degree in the same field from Islamic Azad University. His career spans various teaching, research, and industry roles, reflecting a robust blend of academic prowess and practical expertise.
Profile
Google Scholar
💼 Experience:
His diverse experience includes teaching Quantitative Models and Methods in Logistics and mentoring case study courses. In industry, he serves as a human resource manager and has previously worked in project control management and engineering departments. His voluntary work includes contributions to Mahak, a charity supporting children with cancer.
🔬 Research Interests:
Danyal’s research interests span optimization in mathematical programming, uncertainty programming (robust, fuzzy, and stochastic), data science, machine learning, and supply chain modeling in energy and food sectors. He is particularly focused on decision-making under uncertainty and sustainable supply chain design.
🏆 Awards:
He ranked in the top 1% in the Master’s degree National University Entrance Examination among over 10,000 participants, earning a full scholarship in 2016. Additionally, he secured the second rank in a memorizing competition in Tehran in 2010.
Publications
📄 A Sustainable Integrated Model for Multi-Objective Planning of an Agri-Food Supply Chain under Uncertain Parameters: A Case Study – Accepted at Computers and Chemical Engineering, cited by 5 articles.
📄 A Review of Digital Platform and Circular Economy: Opportunities and Challenges for Developing Countries – Accepted at Taylor & Francis, 2023, cited by 3 articles.
📄 AI Implementation in Innovation Processes: An Uncertainty Perspective – In progress, expected publication in 2024.
📄 A Circular Data-Driven Mathematical Model for Multi-Objective Agri-Food Supply Chain Design Considering GIS Method: Stevia – In progress.📄 A Hybrid Sustainable Biomass Supply Chain Design Using ML Algorithms, GIS Methods, and Robust Modeling – In progress.