Dr Mahdi Shavarani

Dr Mahdi Shavarani is Lecturer in Business Analytics at Kent Business School. 

He obtained a Ph.D. in Industrial Engineering from Eastern Mediterranean University in 2018. He also holds a Ph.D. in Multi-Objective Optimization and Multi-Criteria Decision-Making from the University of Manchester, where he has been working extensively on interactive evolutionary multi-objective optimization algorithms. 

He is an active member of the International Society on Multiple Criteria Decision Making and is affiliated with the Decision and Cognitive Science Research Center at Alliance Manchester Business School.  

His research interests primarily revolve around operations research, with a specific focus on optimization algorithms, logistics, production systems, and the utilization of machine learning techniques in the field of operations research.  

Dr. Seyed Mahdi Shavarani is a dedicated Operational Research/Business Analytics Scientist with a strong focus on optimization and decisionmaking tools. 

His research at the University of Kent centres around the development and implementation of effective and sustainable optimization models to address real-world business analytics challenges, with a particular emphasis on logistics, supply chain management, transportation and manufacturing domains. 

Dr. Shavarani's work encompasses the exploration of multi-criteria decision-making concepts and the integration of machine learning techniques into interactive multi-objective optimization algorithms. By leveraging these approaches, he aims to enhance the efficiency of algorithms in terms of reducing cognitive effort for decision-makers, improving computational efficiency, and achieving higher-quality solutions.
Furthermore, his research involves investigating the dynamics of decision-making behaviors through simulation, providing valuable insights into the performance of interactive methods. 

Finally, Dr. Shavarani's research delves into dimension-reduction techniques. These techniques aim to mitigate the challenges posed by high-dimensional objective spaces, effectively reducing the number of objectives without compromising the quality of the optimizer. 

Through his comprehensive research endeavors, Dr. Shavarani contributes to the advancement of knowledge in the field of operational research and business analytics. His work has practical implications for optimizing real-world systems, fostering sustainable practices, and improving decision-making processes.

Dr. Mahdi Shavarani teaches courses in the field of operations research, systems simulation and analysis.

• Shavarani, S. M., López-Ibáñez, M., & Knowles, J. (2023). On Benchmarking Interactive Evolutionary Multi-Objective Algorithms. IEEE Transactions
• Shavarani, S. M., López-Ibáñez, M., Allmendinger, R., & Knowles, J. (2023, March). An Interactive Decision Tree-Based Evolutionary Multi-objective Algorithm. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 620-634). Cham: Springer Nature Switzerland.
• Xu, S., Shavarani, S. M., Nejad, M. G., Vizvari, B., & Toghraie, D. (2023). A novel competitive exact approach to solve assembly line balancing problems based on lexicographic order of vectors. Heliyon, 9(3).