Electrification of Transportation in smart cities: A multi-objective approach to design and locate charging facilities
Fully funded (UK/EU/International students)
Operations and Systems Management
23 February 2020
Successful applicants will receive a bursary to cover tuition fees at the UK/EU rate for three years and a stipend in line with the RCUK rate (£15,009 for 2019/2020). As part of the bursary, the Faculty of Business and Law may fund fieldwork expenses (currently £2,000) over the total period of PhD study. We also offer funding to attend conferences (currently £550), training (currently £450), and a work-based placement (currently a maximum of £3,000 tied up to the period of 12 weeks).
The work on this project could involve:
- A decision support system design for the planning of electric vehicle charging infrastructure in the UK
- Development of multi-objective optimisation approaches for facility location problems with cost, equity and efficiency objectives
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 . 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 .
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 , 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].
- UK Greenhouse Gas Emissions, Provisional Figures - Statistical Release (2018), https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/790626/2018-provisional-emissions-statistics-report.pdf
- UK Department for Transport (2018) The Road to Zero: Next steps towards cleaner road transport and delivering our Industrial Strategy, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/739460/road-to-zero.pdf
- Barbati, M., and Piccolo, C. (2016). Equality measures properties for location problems. Optimization Letters, 10(5), 903-920.
- Karsu, O., and Morton, A. (2015). Inequity averse optimization in operational research. European journal of operational research, 245(2), 343-359.
- Lokman, B., and Köksalan, M. (2013). Finding all nondominated points of multi-objective integer programs. Journal of Global Optimization, 57(2), 347-365.
- 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.
- 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.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
We welcome applications from highly motivated prospective students with a background in Operations Research (e.g. industrial engineering, business and management, computer science, mathematics and other relevant disciplines) with an interest in multi-criteria decision making. A familiarity with multi-objective optimization and facility location problems are desirable (not essential). We are also interested in candidates who are familiar with the interactive algorithms to solve multi-objective optimization problems. We encourage prospective students to design their own research strategies depending on their interest and core skills.
How to apply
We’d encourage you to contact Dr Banu Lokman at firstname.lastname@example.org to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
Please also include a research proposal of 1,000 words outlining the main features of your proposed research design – including how it meets the stated objectives, the challenges this project may present, and how the work will build on or challenge existing research in the above field.
If you want to be considered for this funded PhD opportunity you must quote project code O&SM5110220 when applying.