Article ID: | iaor2016471 |
Volume: | 24 |
Issue: | 12 |
Start Page Number: | 1907 |
End Page Number: | 1930 |
Publication Date: | Dec 2015 |
Journal: | Production and Operations Management |
Authors: | Rajaram Kumar, Barz Christiane |
Keywords: | allocation: resources, health services, combinatorial optimization, stochastic processes, control processes, programming: markov decision, programming: dynamic, heuristics |
We consider a patient admission problem to a hospital with multiple resource constraints (e.g., OR and beds) and a stochastic evolution of patient care requirements across multiple resources. There is a small but significant proportion of emergency patients who arrive randomly and have to be accepted at the hospital. However, the hospital needs to decide whether to accept, postpone, or even reject the admission from a random stream of non‐emergency elective patients. We formulate the control process as a Markov decision process to maximize expected contribution net of overbooking costs, develop bounds using approximate dynamic programming, and use them to construct heuristics. We test our methods on data from the Ronald Reagan UCLA Medical Center and find that our intuitive newsvendor‐based heuristic performs well across all scenarios.