Distributionally robust appointment scheduling with moment-based ambiguity set

Yiling Zhang, Siqian Shen, S. Ayca Erdogan

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


We study appointment scheduling under random service duration with unknown distributions. Given a sequence of appointments arriving at a single server, we assign their planned arrival time to minimize the expected total waiting time, while using a chance constraint to restrict the probability of server overtime. We consider a distributionally robust formulation based on an ambiguity set that uses the first two moments, and derive an approximate semidefinite programming model. We conduct computational studies by testing outpatient treatment scheduling instances.

Original languageEnglish (US)
Pages (from-to)139-144
Number of pages6
JournalOperations Research Letters
Issue number2
StatePublished - Mar 1 2017
Externally publishedYes

Bibliographical note

Funding Information:
The authors are grateful to the Associate Editor and anonymous reviewers for their helpful comments and suggestions. Dr.?Shen acknowledges partial support by the National Science Foundation under grant CMMI-1433066.

Publisher Copyright:
© 2017 Elsevier B.V.


  • Appointment scheduling
  • Chance-constrained programming
  • Distributionally robust optimization
  • Random service durations
  • Semidefinite programming


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