Distributionally robust appointment scheduling with moment-based ambiguity set

Yiling Zhang; Siqian Shen; S. Ayca Erdogan

doi:10.1016/j.orl.2017.01.010

Distributionally robust appointment scheduling with moment-based ambiguity set

Yiling Zhang, Siqian Shen, S. Ayca Erdogan

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

Abstract

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)
Pages (from-to)	139-144
Number of pages	6
Journal	Operations Research Letters
Volume	45
Issue number	2
DOIs	https://doi.org/10.1016/j.orl.2017.01.010
State	Published - Mar 1 2017
Externally published	Yes

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.

Keywords

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

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abstract = "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.",

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AU - Erdogan, S. Ayca

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AB - 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.

KW - Appointment scheduling

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KW - Distributionally robust optimization

KW - Random service durations

KW - Semidefinite programming

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Distributionally robust appointment scheduling with moment-based ambiguity set

Abstract

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Keywords

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