【Academic Seminar】Intraday Scheduling with Patient Re-entries and Variability in Behaviours - Dr. Gar Goei Loke

Topic: Intraday Scheduling with Patient Re-entries and Variability in Behaviours

Speaker: Dr. Gar Goei Loke, National University of Singapore

Time and Date: 14:00 - 15:00, Wednesday, December 4, 2019

Venue: Boardroom, Dao Yuan Building

Abstract：

We consider the intraday scheduling problem in a group of Orthopaedic clinics where the planner schedules appointment times given a sequence of appointments. We consider patient re-entry – where patients may be required to go for an X-ray examination, returning to the same doctor they have seen – and variability in patient behaviours such as walk-ins, lateness, and no-shows, which leads to inefficiency such as long patient waiting time and physician overtime. Our data indicates that patient re-entry and variability in patient behaviour are prevalent in practice. We formulate the problem as a two-stage optimization problem, where scheduling decisions are made in the first stage. Queue dynamics in the second stage is modelled under a P-Queue (Bandi and Loke 2018) paradigm which minimizes a risk index, representing the chance of violating performance targets such as patient waiting times. The model reduces to a sequence of mixed-integer linear optimization problems. Simulations show that our model can achieve as much as 15% reduction on various metrics including patient waiting time and server overtime over the benchmark policy.

If time permits, I will also share more on the machinery behind the P-Queue paradigm introduced in Bandi and Loke (2018).

Biography：

I am currently a Visiting Assistant Professor with the Department of Analytics & Operations in NUS Business School. I finished my PhD earlier this year with the Department of Mathematics, NUS, under Prof Toh Kim Chuan and Prof Melvyn Sim. I have also worked in the Singapore government. For two of these five years, I served as a Team Lead to a team of data scientists. I retain close ties with them – I am currently a Technical Mentor for the Data Science and Artificial Intelligence Division at the Government Technology Agency.

My research interests are aligned towards multi-period decision-making under uncertainty, with a particular focus on the risk-based notion of Satisficing. Most of my research is in the domain of public policy – healthcare, manpower planning, utilities. In more recent endeavours, I am starting a new stream of work into the interface between machine learning and operations research, and diversifying into domain areas of retail operations.