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Artificial Intelligence Solutions for Home Healthcare Planning (Advert Reference: SF19/BL/MOS/QU2)


Project Description

As populations age in developed countries such as the UK, so the demand increases on already overstretched healthcare services. One way that hospitals are reducing this pressure is by providing healthcare at patients’ homes. Every day healthcare workers visit patients at their homes to provide regular, scheduled treatment. Receiving care and treatment at home is preferable for the patients and frees up valuable resources in clinics and hospitals. The planning, scheduling and routing of home healthcare workers is however a very challenging mathematical problem. It is in fact a combinatorial optimisation problem which for which there is no known solution [1]. The problem is complex because it involves the minimisation of travel times for staff, whilst satisfying the employees working preferences and legal directives whilst also trying to maximise the number of people who can be scheduled for treatment [2].

Artificial Intelligence (AI) methods have already been shown to be effective for similar problems [3,4,5]. Using AI reduces the burden on human planners and provides 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.

This multi-disciplinary research project will build on recent advances from the fields of operations research and computer science to research and develop solutions to this challenging and widely occurring problem.

This project is supervised by Dr Yi Qu and Professor Xuemei Bian.

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: https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

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.

Funding Notes

Please note this is a self-funded project and does not include tuition fees or stipend.

References

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. (2019). Solving Nurse Rostering Problems with Long Planning Horizons Using Branch and Price. Under Journal Review.

References

1. Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2016). Workforce scheduling and routing problems: literature survey and computational study. Annals of Operations Research, 239(1), 39-67.

2. Pinheiro, R. L., Landa-Silva, D., & Atkin, J. (2016). A variable neighbourhood search for the workforce scheduling and routing problem. In Advances in Nature and Biologically Inspired Computing (pp. 247-259). Springer, Cham.

3. 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.

4. Qu, Y., & Curtois, T. (2017). Job Insertion for the Pickup and Delivery Problem with Time Windows. Lecture Notes in Management Science, 9, 26-32.

5. Strandmark, P., Qu, Y., & Curtois, T. (2018). Solving Nurse Rostering Problems with Long Planning Horizons Using Branch and Price. Under Journal Review.

6. Qu, Y., & Curtois, T. (2018). A hybrid branch and price method and new benchmark instances for the nurse rostering problem. Under Journal Review.

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