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Cited by 276

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery

Gerchak, Yigal, Gupta, Diwakar, Henig, Mordechai·1996·Management Science

Abstract

This work concerns the advance scheduling of elective surgery when the operating rooms' capacity utilization by emergency surgery, as well as by elective procedures, is uncertain. New requests for bookings of elective surgery arrive each day. Such procedures preferably would be performed as soon as possible, but admitting too many patients may result in exceeding a day's capacity, possibly necessitating turning away some emergency cases. So the problem facing the hospital at the start of each day is how many of the additional requests for elective surgery to assign for that day. We provide a stochastic dynamic programming model for this aggregate advance scheduling problem. The model has some novel mathematical features. We analyze it and characterize the nature of the optimal policy, which is not necessarily of a control-limit type. Plausible numerical examples which confirm our theoretical results and provide additional insights are reported.

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Metadata

Authors
Gerchak, Y., Gupta, D., & Henig, M.
Venue
Management Science
Published
1996-03
Volume
42
Issue
3
Pages
321-334
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
OpenAlex
W2014320990
Cited by
276
References
11
FWCI
4.02
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery. https://app.cochat.ai/papers/10.1287/mnsc.42.3.321
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Gerchak, Y., Gupta, D., & Henig, M. (1996). Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery. Management Science. https://doi.org/10.1287/mnsc.42.3.321

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.