Distributionally robust appointment scheduling with moment-based ambiguity set
| Autor: | S. Ayca Erdogan, Yiling Zhang, Siqian Shen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Waiting time
Semidefinite programming Mathematical optimization 021103 operations research Computer science Applied Mathematics media_common.quotation_subject 0211 other engineering and technologies Single server 0102 computer and information sciences 02 engineering and technology Ambiguity Appointment scheduling Management Science and Operations Research 01 natural sciences Arrival time Industrial and Manufacturing Engineering Scheduling (computing) 010201 computation theory & mathematics restrict Software media_common |
| Zdroj: | Operations Research Letters. 45:139-144 |
| ISSN: | 0167-6377 |
| DOI: | 10.1016/j.orl.2017.01.010 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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