Optimal Dynamic Appointment Scheduling of Base and Surge Capacity
Benjamin Grant, Itai Gurvich, R. Kannan Mutharasan, Jan A. Van Mieghem
Manufacturing & Service Operations Management · 2022 · Volume 24 · Issue 1
Abstract
Problem definition : We study dynamic stochastic appointment scheduling when delaying appointments increases the risk of incurring costly failures, such as readmissions in healthcare or engine failures in preventative maintenance. When near-term base appointment capacity is full, the scheduler faces a trade-off between delaying an appointment at the risk of costly failures versus the additional cost of scheduling the appointment sooner using surge capacity. Academic/practical relevance : Most appointment-scheduling literature in operations focuses on the trade-off between waiting times and utilization. In contrast, we analyze preventative appointment scheduling and its impact on the broader service-supply network when the firm is responsible for service and failure costs. Methodology : We adopt a stochastic dynamic programming (DP) formulation to characterize the optimal scheduling policy and evaluate heuristics. Results : We present sufficient conditions for the optimality of simple policies. When analytical solutions are intractable, we solve the DP numerically and present optimality gaps for several practical policies in a healthcare setting. Managerial implications : Intuitive appointment policies used in practice are robust under moderate capacity utilization, but their optimality gap can quadruple under high load.
Keywords
transitional care, appointment scheduling, healthcare, preventive maintenance
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