Portrait of Dr Lina Simeonova

Dr Lina Simeonova

Lecturer in Operations Management


Lina Simeonova is a Lecturer in Operations Management at Kent Business School. She was awarded a PhD in Management Science in 2016 from the University of Kent. Previously she has worked as a Project Manager and Researcher on a Knowledge Transfer Partnership, where she worked towards bridging the gap between industry and academia by successfully implementing Operational Research techniques in real settings. 

She was a lead researcher for Kent Police Headquarters on a large-scale multinational project for fighting online crime, focusing on the development of State-of-the-Art algorithms for crime prevention, which were disseminated internationally. She has participated in various events across Kent aimed at encouraging and preparing young students to enrol into higher education.  

Research interests

Lina’s research is mainly in the area of Management Science and Operations Research, with a focus on routing and scheduling, and exploring the benefits of integrated operations in logistics and supply chain.
She finds inspiration for her research from real-life operations and aims to research problems, which are practically applicable and have a positive impact on businesses, society and the environment.


Lina has teaching experience on all academic levels, from primary school to Master’s level in a variety of subjects, including Operations Management, Supply Chain, Spreadsheet Modelling, Data Mining and Forecasting. 

Her aim is to deliver all lectures in an academically informative manner, but also to show the relevance of the teaching material in an industry context.  


Lina has provided real-life projects and co-supervised dissertations of MSc students for a number of years.


Lina is certified in a number of professional qualifications, including Prince2 Project Management, Agile Project Management, SQL Databases and holds a Green Belt for Lean Six Sigma.



  • Simeonova, L., Wassan, N., Salhi, S. and Nagy, G. (2018). The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory. Expert Systems with Applications [Online] 114:183-195. Available at: https://dx.doi.org/10.1016/j.eswa.2018.07.034.
    In this paper we consider a real life Vehicle Routing Problem inspired by the gas delivery industry in the United Kingdom. The problem is characterized by heterogeneous vehicle fleet, demand-dependent service times, maximum allowable overtime and a special light load requirement. A mathematical formulation of the problem is developed and optimal solutions for small sized instances are found. A new learning-based Population Variable Neighbourhood Search algorithm is designed to address this real life logistic problem. To the best of our knowledge Adaptive Memory has not been hybridized with a classical iterative memoryless method. In this paper we devise and analyse empirically a new and effective hybridization search that considers both memory extraction and exploitation. In terms of practical implications, we show that on a daily basis up to 8% cost savings on average can be achieved when overtime and light load requirements are considered in the decision making process. Moreover, accommodating for allowable overtime has shown to yield 12% better average utilization of the driver's working hours and 12.5% better average utilization of the vehicle load, without a significant increase in running costs. We also further discuss some managerial insights and trade-offs.


  • Naveed, W., Niaz, W., Lina, S. and Walid, B. (2019). Green Reverse Logistics: Case of the Vehicle Routing Problem with delivery and collection demands. In: Walid, B., Diala, D., Niaz, W. and Amna, M. eds. Optimization of Green Transportation Problems: Fundamentals and Applications. France: Wiley (English) and ISTE (French).
    The Chapter discusses the developments in the area of vehicle routing problem (VRP) and more specifically the VRPs, which feature both delivery and collection demands, and their relevance and contribution to the go green reverse logistics polices. The chapter initially elaborates on the VRP and its variants involved in reverse logistics followed by highlighting the significance and ecological relevance of the VRP delivery and collection models and provides information concerning freight transport CO2 conversions and computations. Finally, the chapter describes green VRP models, showing the link between traditional and green VRP models and how principles from both could be combined in order to achieve better economic and environmental gains.
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