Funded PhD opportunities
Advanced Optimisation Models and Meta-heuristics for Patient Scheduling in Dental Services
- Application end date: 11th February 2018
- Funding Availability: Funded PhD project (EU/UK/International)
- Department: Mathematics
- PhD Supervisor: Professor Djamila Ouelhadj, Dr. Kristina Wanyonyi
Project code: TBC
The project addresses the challenge of patient scheduling within dental services. The Dental Academy at the University of Portsmouth will be one case study site for this project, which will compare scheduling in multiple sites. The project is a cross-faculty collaboration between the Logistics, Operational Research, and Analytics Group, Mathematics Department and the Dental Academy at the University of Portsmouth. Currently, the Dental Academy operates a National Health Service contract similar to high street dental practices across the UK. The scheduling of patients to undertake a dental treatment is usually generated by manually assigning the time slots to patients considering the type of treatment, the processing time of the treatment, and the workforce required to deliver the treatment. The manual procedure is inefficient due to the increased demand and the complexity of the constraints. Inefficient scheduling is likely to lead to losses of time and cost to patients, dental practices and NHS services.
The main challenge of this project is to automate the patient scheduling procedure in order to generate an efficient schedule to assign patient appointments to time slots, considering one or more objectives, such as minimising patients’ average waiting time, dental equipments’/dentists’ average idle time, overtime, and cost. Patient’s waiting time refers to the time between the patient’s arrival time and the actual service start time of the patient’s service/appointment. Patients’ waiting time is affected by the tardiness of earlier patients, causing a delay in the scheduled treatment time, and patients having different treatment times. For example, if a patient finishes his/her treatment later than the scheduled time, subsequent patients would finish their treatment beyond the originally scheduled time. Consequently, the patient must wait longer for their treatment. However, if the patient arrives punctually, patients’ waiting time will not be an issue. Idle time in appointment scheduling refers to the time when staff, equipment, or resources are not being used. If a scheduler assigns too few patients to a time slot, medical resources will stand idle. However, if a scheduler assigns too many patients to a time slot, some patients will be forced to wait. Thus, a trade-off exists between patients’ waiting time and resources’ idle time. Moreover, the schedule is subject to uncertainties such as overbooking, no-shows, and tardy patients.
The aim of this project is to identify the key drivers that drive efficient scheduling in NHS dental services, develop novel optimisation models, meta-heuristics methods, and sim-optimisation methods for an efficient scheduling of the appointments of patients in the presence of uncertainties.
You’ll need a good first degree from an internationally recognised university (depending upon chosen course, minimum second class or equivalent) or a Master’s degree in an appropriate subject. Exceptionally, equivalent professional experience and/or qualifications will be considered. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
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How to Apply
You can apply online at www.port.ac.uk/applyonline. You are required to create an account which gives you the flexibility to save the form, log out and return to it at any time convenient to you.
A link to the online application form and comprehensive guidance notes can be found at www.port.ac.uk/pgapply.
When applying, please quote project code: TBC.
Interview date: TBC
Start date: October 2018.
The fully-funded, full-time three-year studentship provides a stipend that is in line with that offered by Research Councils UK of £14,553 per annum.
The above applies for Home/EU students only.
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