Optimal Dynamic Appointment Scheduling of Base and Surge Capacity

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)59-76
Number of pages18
JournalManufacturing and Service Operations Management
Issue number1
StatePublished - Jan 2022


  • Appointment scheduling
  • Healthcare
  • Preventive maintenance
  • Transitional care

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research


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