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Applications are invited for a self-funded, 3-year full-time or 6-year part-time PhD project, to commence in October or February.

The PhD will be based in the Faculty of Business and Law, and will be supervised by Dr Banu Lokman. Prof Murat Koksalan will be involved as a third, external supervisor. 

Electromobility in transportation (EiT) is becoming more important with the increasing concerns on global warming. EiT not only reduces the carbon dioxide emissions, air pollution, and noise but also improves energy efficiency.

Transportation accounted for 33% of all carbon dioxide emissions in 2018 [1]. Since road transportation is the fundamental way of moving people and cargo across the UK, a substantial portion of total emissions from transportation is caused by road transportation [2].

Therefore, vehicle EiT provides a huge potential towards cleaner and green transportation. On the other hand, the market shares of battery-powered electric vehicles (EVs) and hybrid electric vehicles in the UK are still quite small. This is due to the limited number of charging facilities and mileage concerns of the customers.

The aim of this project is to design a decision support system for locating charging stations to support long-distance travel by EVs. Different from the existing approaches, in addition to cost concerns, this project will take equity concepts into consideration to develop a multi-period multi-objective optimization model that provides low cost solutions with a fair distribution of the charging services among different regions [3,4].

Since a unique solution to multi-objective optimization problems does not usually exist [5], the project will develop an algorithm to generate desirable efficient solutions by following an interactive solution strategy that incorporates the preferences of policy makers [6,7]. 

References:

1. UK Greenhouse Gas Emissions, Provisional Figures – Statistical Release (2018),
2. UK Department for Transport (2018) The Road to Zero: Next steps towards cleaner road transport and delivering our Industrial Strategy,
3. Barbati, M., and Piccolo, C. (2016). Equality measures properties for location problems. Optimization Letters, 10(5), 903–920. 
4. Karsu, O., and Morton, A. (2015). Inequity averse optimization in operational research. European journal of operational research, 245(2), 343–359.
5. Lokman, B., and Köksalan, M. (2013). Finding all nondominated points of multi-objective integer programs. Journal of Global Optimization, 57(2), 347–365.
6. Lokman, B., Köksalan, M., Korhonen, P. J., & Wallenius, J. (2016). An interactive algorithm to find the most preferred solution of multi-objective integer programs. Annals of operations research, 245(1–2), 67–95.
7. Ceyhan, G., Köksalan, M., & Lokman, B. (2019). Finding a representative nondominated set for multi-objective mixed integer programs. European Journal of Operational Research, 272(1), 61–77.

Fees and funding

Visit the research subject area page for fees and funding information for this project.

PhD full-time and part-time courses are eligible for the .

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.