Determination of server location in emergency care systems: an index proposal using data envelopment analysis and the hypercube queuing model

被引:0
|
作者
Mariano E.B. [1 ]
Siqueira R.M. [1 ]
Beojone C.V. [2 ]
Luiz O.R. [1 ,3 ]
Luiz J.V.R. [1 ]
dos Santos H.H. [1 ]
机构
[1] Department of Production Engineering, College of Engineering, São Paulo State University (UNESP), São Paulo, Bauru
[2] Urban Transport Systems Laboratory (LUTS), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne
[3] Department of Engineering, Institute of Sciences and Engineering, São Paulo State University (Unesp), São Paulo, Itapeva
基金
巴西圣保罗研究基金会;
关键词
Benefit of the doubt; Data envelopment analysis; Emergency care systems; Hypercube queuing model; Location problems; Queuing theory;
D O I
10.7717/PEERJ-CS.1637
中图分类号
学科分类号
摘要
The objective of this article is to propose a new composite index (CI) that helps to determine the most effective location of servers in an Emergency Care System (ECS), using Benefit of the Doubt (BoD)/Data Envelopment Analysis (DEA) and the Hypercube queuing model. The CI proposed was developed in four stages: (1) definition of a number of possible ECS configurations through the application of mathematical partitions and permutations; (2) application of the hypercube queuing model to determine performance parameters for each ECS configuration; (3) application of DEA/BoD to build the CI and generate performance rankings, and (4) evaluation of the rankings obtained to define the best configuration for the ECS analyzed. Data from two real cases from Brazil were used to assess the CI proposal. The results obtained confirm that: (a) the hypercube model could, relatively quickly, determine the configuration parameters generated; (b) the application of an appropriate DEA/BoD model enabled the different configurations to be ranked with good discrimination; (c) a pattern in the relationship between ambulance concentration and configuration effectiveness could be identified; and (d) the CI proposed would benefit ECS managers who are making resource location decisions. © 2023 Mariano et al.
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