The design of a reliable and robust hierarchical health service network using an accelerated Benders decomposition algorithm

•A reliable health service network design model is developed.•The model is able to consider the uncertainty associated with input parameters.•The patients expected waiting time is considered in the model by a queuing system.•To solve the model, an accelerated Benders decomposition algorithm is propo...

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Veröffentlicht in:European journal of operational research Jg. 265; H. 3; S. 1013 - 1032
Hauptverfasser: Zarrinpoor, Naeme, Fallahnezhad, Mohammad Saber, Pishvaee, Mir Saman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 16.03.2018
Schlagworte:
Benders decomposition
Disruption
OR in Health services
Priority queuing system
Robust scenario-based stochastic programming
OR in Health services
Robust scenario-based stochastic programming
Disruption
Benders decomposition
Priority queuing system
ISSN:0377-2217, 1872-6860
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Zusammenfassung:•A reliable health service network design model is developed.•The model is able to consider the uncertainty associated with input parameters.•The patients expected waiting time is considered in the model by a queuing system.•To solve the model, an accelerated Benders decomposition algorithm is proposed. This paper proposes a novel reliable hierarchical location-allocation model addressing a real-world health service network design problem. The model considers several key issues regarding health service network designs such as hierarchical structure of networks characterised by a two-level multi-flow hierarchy with service referral, uncertainty associated with demand, service and geographical accessibility, prioritizing patients based on their urgency and adopting different service strategies to serve them, service quality reflected in the patients’ expected waiting time by considering the priority queuing system, and risk of unexpected disruptive events. To deal with different sources of uncertainty in the concerned problem, a robust scenario-based stochastic programming approach is employed. To solve the proposed model, a Benders decomposition algorithm enhanced by several accelerating methods is developed. A practical case study is presented to illustrate the applicability of the proposed model as well as the effectiveness of the designed solution procedure.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2017.08.023