Intraday Scheduling with Patient Re-entries and Variability in Behaviours

Minglong Zhou (), Gar Goei Loke (), Chaithanya Bandi (), Zi Qiang Glen Liau () and Wilson Wang ()
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Minglong Zhou: Department of Analytics and Operations, NUS Business School, National University of Singapore, Singapore 119245
Gar Goei Loke: Department of Analytics and Operations, NUS Business School, National University of Singapore, Singapore 119245
Chaithanya Bandi: Department of Analytics and Operations, NUS Business School, National University of Singapore, Singapore 119245
Zi Qiang Glen Liau: University Orthopaedics, Hand and Reconstructive Microsurgery Cluster, National University Health System, Singapore 119228
Wilson Wang: University Orthopaedics, Hand and Reconstructive Microsurgery Cluster, National University Health System, Singapore 119228

Manufacturing & Service Operations Management, 2022, vol. 24, issue 1, 561-579

Abstract: Problem definition : 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, earliness, and no-shows, which leads to inefficiency such as long patient waiting time and physician overtime. Academic/practical relevance : In our data set, 25% of the patients are required to go for x-ray examination. We also found significant variability in patient behaviours. Hence, patient re-entry and variability in behaviours are common, but we found little in the literature that could handle them. Methodology : 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 are modeled under a P-Queue 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. Results : Our model achieves significant reductions, in comparative studies against a sample average approximation (SAA) model, on patient waiting times, while keeping server overtime constant. Our simulations further characterize the types of uncertainties under which SAA performs poorly. Managerial insights : We present an optimization model that is easy to implement in practice and tractable to compute. Our simulations indicate that not accounting for patient re-entry or variability in patient behaviours will lead to suboptimal policies, especially when they have specific structure that should be considered.

Keywords: optimization; scheduling (search for similar items in EconPapers)
Date: 2022
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