Doctoral research
If you're interested in joining a dynamic community of talented researchers from around the world to explore research questions that matter, we would like to hear from you.
Our research topics
Within the Management Science and Operations group we are especially keen to receive PhD research proposals in the following areas:
- improving management by designing and implementing analytic approaches that help tackle routine, strategic or policy problems;
- developing analytical models that can often be represented mathematically or visually and built using specialist software;
- contributing analytical insights and solutions to a range of problems within domains such as health, transport, logistics and supply chain management, process improvement, group decision making and emergency management.
The group has great supervisory experience and is keen to supervise high-quality research students in members’ specialist research fields.
Please feel free to browse our group members to identify a potential supervisor(s), develop your research proposal, share this with the identified supervisor(s) and confirm they are willing to be named in support of your application. You are encouraged to ask for feedback from them to develop your research proposal. Then you should be ready to formally apply.
Proposed PhD projects
Development of a tool to formulate artificial intelligence implementation roadmaps for SMEs
Project reference
LB24-UJ (please quote this in your application).
Proposers
Project description
Over 99% of businesses in the UK are SMEs. In 2022, 82% of SMEs lacked the skills, resources and knowledge of planning and adopting advanced technologies such as AI at the right time in the organisational lifecycle. Hence, there are massive inefficiencies and wastage in operating in a dynamic business environment, eventually leading to bankruptcy. Therefore, it is important to identify and prioritise applications of AI that highly affect SMEs and go further to develop a parametrised software tool to formulate AI implementation roadmaps for SMEs to overcome business failures and minimise the cost of operation.
This study aims to develop the above-mentioned software tool to formulate AI implementation roadmaps for SMEs. The study has a greater impact on SMEs in defining their future and understanding the right route to be taken in achieving business goals using AI.
There are a few SMEs, particularly medium-sized enterprises, who are tapping into AI for business excellence. With the rapid advancement of technology, use of AI is becoming not only limited to large organisations. The adoption of AI will be a must for SMEs in no time. Therefore, this project will help to bridge the lack of digitalisation in SMEs by enabling applications of AI in SMEs which is of high national and international importance. Thereby, you will be able to get involved in one of the very few projects which investigates this niche area of AI in SMEs. This will be a great chance to take part in a project that will have real-world impact. This project and the chance to work with world-leading experts will help to upskill yourself and your career progression.
Electric vehicle routing to minimise the risk of missing appointments: enhancing heuristics with reinforcement learning
Project reference
LB24-RM (please quote this in your application).
Proposers
Project description
The widespread adoption of electric vehicles (EVs) is transforming the field service industry, offering a more sustainable and cost-effective approach to service delivery. However, effectively routing EVs presents unique challenges due to their limited range and charging requirements. This research aims to develop innovative solutions for routing EVs in field service operations, ensuring that technicians can reach all their appointments without running out of charge and maximizing productivity.
The key research objectives are:
- to analyse raw data from a large service provider and develop models and algorithms that consider travel time variability, service time uncertainty and charging station availability, to minimize the risk of missed appointments as well as operation cost including energy consumption cost
- to implement dynamic routing algorithms that can adjust routes and technician schedules in real-time to respond to unexpected events such as traffic congestion, customer cancellations and service time changes
- to integrate reinforcement learning techniques with established vehicle routing heuristics for enhanced efficiency in solving the problem.
The expected benefits of the research include considerably improved customer satisfaction by minimizing missed appointments and ensuring timely service delivery. The transition to EVs and optimized routing will also significantly reduce greenhouse gas emissions and promote a more sustainable future. This research holds the potential to improve field service operations, enabling companies to enhance customer satisfaction and contribute to a more sustainable future.