Artificial Intelligence Solutions for Home Healthcare Planning (SF19/MOS/QU2)
Every day thousands of healthcare workers visit patients at their homes to provide regular, scheduled treatment. Receiving care at home is preferable for the patients and frees up valuable resources in clinics and hospitals. As populations age in developed countries such as the UK, so more and more people will continue to receive treatment at home. Providing care at home is one way to ease the pressure on stretched hospital budgets. The planning, scheduling and routing of home healthcare workers is however a very challenging problem. It is in fact a combinatorial optimisation problem which for which there is no known solution . The problem is complex because it involves the minimisation of travel times for staff, whilst respecting the employees working preferences and constraints whilst also trying to maximise the number of people who can be scheduled for treatment .
Artificial Intelligence (AI) methods have already been shown to be effective for similar problems [3,4,5]. Using AI will reduce the burden on human planners and provide better solutions. By planning the schedules and routes more efficiently it will free up more time. This time can be used by the nurses to do what they want to be doing: helping those that need it. Not travelling between locations, waiting in traffic or just waiting for their next appointment which has been scheduled unnecessarily late.
The project will look to work with a local NHS Trust which has already expressed an interest to collaborate. It will look to solve real-world problems from the start in order to have a clear and direct impact.
Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/…) will not be considered.
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.
Please note this is a self-funded project and does not include tuition fees or stipend.
Recent publications by supervisors relevant to this project:
Qu, Y., & Curtois, T. (2017). Job Insertion for the Pickup and Delivery Problem with Time Windows. Lecture Notes in Management Science, 9, 26-32.
Curtois, T., Landa-Silva, D., Qu, Y., & Laesanklang, W. (2018). Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows. EURO Journal on Transportation and Logistics, 1-42.
Qu, Y., & Curtois, T. (2018). A hybrid branch and price method and new benchmark instances for the nurse rostering problem. Under Journal Review.
Strandmark, P., Qu, Y., & Curtois, T. (2018). Solving Nurse Rostering Problems with Long Planning Horizons Using Branch and Price. Under Journal Review.