A two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty

Abstract Surgery demand is an uncertain parameter in addressing the problem of surgery block allocations, and its typical variability should be considered to ensure the feasibility of surgical planning. We develop two models, a stochastic recourse programming model and a two-stage stochastic optimization (SO) model with incorporated risk measure terms in the objective functions to determine a planning decision that is made to allocate surgical specialties to operating rooms (ORs). Our aim is to minimize the costs associated with postponements and unscheduled demands as well as the inefficient use of OR capacity. The results of these models are compared using a case of a real-life hospital to determine which model better copes with uncertainty. We propose a novel framework to transform the SO model based on its deterministic counterpart. Three SO models are proposed with respect to the variability and infeasibility of the measures of the objective function to encode the construction of the SO framework. The analysis of the experimental results demonstrates that the SO model offers better performance under a highly volatile demand environment than the recourse model. The originality of this work lies in its use of SO transformation framework and its development of stochastic models to address the problem of surgery capacity allocation based on a real case..

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:26

Enthalten in:

Health care management science - 26(2023), 2 vom: 27. Mai, Seite 238-260

Sprache:

Englisch

Beteiligte Personen:

Lalmazloumian, Morteza [VerfasserIn]
Baki, M. Fazle [VerfasserIn]
Ahmadi, Majid [VerfasserIn]

Links:

Volltext [lizenzpflichtig]

BKL:

44.05$jGesundheitsökonomie

Themen:

Demand uncertainty
Operating room planning
Operations management
Operations research
Scheduling
Stochastic optimization
Surgery capacity allocation
Two-stage stochastic programming

Anmerkungen:

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

doi:

10.1007/s10729-023-09644-5

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2143802927

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