Dr Mahdi Shavarani

Dr Mahdi Shavarani is a Lecturer in Business Analytics in the Department of Analytics, Operations and Systems at Kent Business School. His work sits at the intersection of operational research, optimisation, decision science and artificial intelligence, with a focus on complex decisions involving multiple objectives, uncertainty, limited resources and competing stakeholder priorities.

A major strand of his research concerns human-in-the-loop and preference-aware optimisation: developing algorithms that can learn and adapt to what decision-makers value rather than assuming that their objectives and preferences are fully known in advance. His research in this area has been published in IEEE Transactions on Evolutionary Computation.

His work combines methodological development with applications across production and manufacturing, logistics and supply chains, transport and mobility, retail, healthcare, humanitarian operations and infrastructure. He has collaborated with industry and external organisations on operational and strategic decision problems including production planning, routing and scheduling, resource allocation, logistics, retail decision-making, transport and infrastructure planning.

He holds a PhD in Business and Management from the University of Manchester and a PhD in Industrial Engineering from Eastern Mediterranean University. He is a Senior Fellow of Advance HE and a member of the Centre for Logistics and Sustainability Analytics (CeLSA), The OR Society, and the International Society on Multiple Criteria Decision Making. 

He was also endorsed by the British Academy under the UK Global Talent Exceptional Promise category.  

Dr Shavarani's research interests include interactive and preference-aware multi-objective optimisation; multi-criteria decision making; preference learning and human-in-the-loop decision making; mathematical optimisation and metaheuristics; machine learning within optimisation; simulation and stochastic modelling; facility location and network design; routing, scheduling and resource allocation; production and manufacturing optimisation; and decision-support systems.

His current research particularly examines how optimisation algorithms can learn from decision-makers when objectives conflict, preferences evolve over time, or priorities cannot be completely specified in advance.  

Industry and External Engagement

Dr Shavarani collaborates with industry and external organisations on operational and strategic decision problems. His applied work focuses on helping organisations allocate resources, improve operational systems, evaluate competing alternatives and make better evidence-based decisions under uncertainty and operational constraints.
He is particularly interested in collaborative research and knowledge-exchange projects where operational research, optimisation, analytics and decision science can be used to address complex organisational problems.  

Dr Shavarani teaches optimisation, simulation, decision modelling and applied business analytics at undergraduate and postgraduate levels. His teaching combines analytical methods with practical modelling and computational tools, including Python, CPLEX and OPL.

He is a Senior Fellow of the Higher Education Academy (SFHEA) and holds a Postgraduate Certificate in Higher Education from the University of Kent.  

Dr Shavarani welcomes enquiries from prospective PhD students in optimisation, operational research, decision science and business analytics.

Areas of particular interest include interactive and preference-aware optimisation, multi-objective and multi-criteria decision making, preference learning, optimisation and machine learning, facility location and network design, routing and scheduling, production systems, logistics and supply-chain optimisation, resource allocation, and transport and infrastructure decision support.  

• 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).