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 language||English (US)|
|Number of pages||6|
|Journal||Operations Research Letters|
|State||Published - Mar 1 2017|
Bibliographical noteFunding 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.
© 2017 Elsevier B.V.
- Appointment scheduling
- Chance-constrained programming
- Distributionally robust optimization
- Random service durations
- Semidefinite programming