Optimal Dynamic Appointment Scheduling of Base and Surge Capacity

Benjamin Grant (), Itai Gurvich (), R. Kannan Mutharasan () and Jan A. Van Mieghem ()
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Benjamin Grant: Department of Management, Clemson University, Clemson, South Carolina 29634
Itai Gurvich: Operations Research and Information Engineering Department, Cornell Tech, New York, New York 10044
R. Kannan Mutharasan: Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611
Jan A. Van Mieghem: Department of Operations, Kellogg School of Management at Northwestern University, Evanston, Illinois 60208

Manufacturing & Service Operations Management, 2022, vol. 24, issue 1, 59-76

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 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:24:y:2022:i:1:p:59-76

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