The relocation of ambulance stations: A successful case study

McAleer, W.E.; Naqvi, I.A.

doi:10.1016/0377-2217(94)90298-4

Cited by 17 publications

(5 citation statements)

References 3 publications

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“…Therefore, they reached a number of acceptable solutions in each part. They then examined all possible combinations of acceptable sites using the 54 demand points [7]. Osinuga et al developed an integrated methodology in the field of healthcare location that minimizes the weighted average Euclidean distance between existing facilities and the new facility in a way that demands will not have to travel a very long distance to access medical facilities.…”

Section: Accessibilitymentioning

confidence: 99%

A Novel Robust Model for Health Care Facilities Location-Allocation Considering Pre Disaster and Post Disaster Characteristics

et al. 2021

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Health care centers are one of the most important municipal facilities that are involved in providing personal and social health. Locating medical centers is an important strategy to improve these facilities performance and reduce the relief process delay. On the other hand, during disasters, these centers have a critical role in the human casualty's reduction. In this paper, a new Robust Mathematical Modeling is simultaneously provided to locate and allocate healthcare facilities, including different service levels of medical care, concerning the characteristics in normal and disaster situations. Furthermore, to help victims and prevent overcrowding in medical centers during disasters, the establishment of temporary and outpatient centers is allowed for emergency provision of basic services. Also, the possibility to send equipment and medical teams to these centers is considered. To solve the problem, two metaheuristic algorithms, harmony search algorithm and hybrid Tabu search combined with variable neighborhood search algorithm, and a lower bound based on Lagrangian relaxation method are presented. To examine the proposed algorithms, sample problems are randomly generated. The results of exact solutions and the lower bound resulting from the Lagrangian relaxation method are compared with the meta-heuristic algorithms results. The results show the good performance of the algorithms.

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Section: Accessibilitymentioning

confidence: 99%

A Novel Robust Model for Health Care Facilities Location-Allocation Considering Pre Disaster and Post Disaster Characteristics

et al. 2021

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“…Thus, using a Z ∼ N (0, 1) -reduced normal , does not bring significant results, because it imputes P (Y ≤ 0), a 0, 0001 probability -only 10 times smaller than the order of magnitude of (1 − p) = 0, 001, to an event that surely can never happen. Consequently, for bigger improvements in the pre-hospital care services (MCALEER and NAQVI, 1994) and (TAVAKOLI and LIGHTNER, 2003) suggest that the regions corresponding to a city division must be smaller in a way that: h ↓ implies a P (Y ≤ 0) ↑. In order to correct the distortion of P (Y ≤ 0), it is necessary to adopt the following procedure: instead of Y being treated as a normal Z ∼ N (0, 1), treating Y as a truncated normal, which probability density function (p.d.f.)…”

Section: Dimensioning Of Stations/integration Centers -Hospital Bedsmentioning

confidence: 99%

Optimizing the Pre-Hospital Mobile Care System: A Case Study

Cordeiro

Cunha

2018

Rev. Bras. Biom.

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Freidenfields (1980) introduced for the modeling of several transport systems demands the concepts of queueing theories and studied the problem of the capacity expansion of the transport system as a random process of life and death, showing that it is possible to adapt the stochastic model of demand growth into a deterministic model. Souza (1996) applied this theory to predict the expansion of the emergency care systems. The modeling of the supply of Us integrated into the hospitals - emergency care and inter-hospital removals - despite being considered a restricted market service, as new solutions are developed new knowledge is aggregated into an increasingly lower cost (GOLDBERG, 2004). The dimensioning, allocation and distribution of the supply of Us developed for the pre-hospital mobile care system, utilizing data based on the Brazilian situation, is a field that deserves extreme attention. That will allow the assessment of the present situation and can lead to new routes in terms of public policies. Thus, the distribution of service stations of the regulation centers represents the ordering and orienting element of the State Systems of Urgency and Emergency. These centers must be structured in all levels, organizing the relation between several services, qualifying the flux of patients in the system and generating an integrative gateway for the hospitals, by which distress signals are received, evaluated and ranked. These rules must be followed by all services, both public or private. It can be mentioned, as an example, that for the emergency services a widely used measure is the maximization use of the Us or the minimization of response time TR, between any user of the transport system and the nearest hospital.

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“…Relocation decisions are thus longterm strategic decisions which occur rather infrequently. However, when such a decision needs to be made, the factors influencing it are best analyzed by a decision support system [25].…”

Section: Emergency Vehiclesmentioning

confidence: 99%

Decision Support Systems in Health Care

Walus¹,

Ittmann²,

Hanmer³

1997

Methods Inf Med

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Abstract:This paper delineates the application of decision support systems in the healthcare arena. An overview of decision support systems in the healthcare environment in the last three to five years, mostly in the USA and the UK is provided. Some reference is made to work in South Africa. Although this report endeavours to emphasise the decision support system side, some operations research issues are also addressed. This paper is divided into several sections: decision support systems definition, techniques used, the use of operations research in specific areas of healthcare, information systems, assessment of decision support systems, in-house development, decision support in South Africa and future of decision support systems.

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The relocation of ambulance stations: A successful case study

Cited by 17 publications

References 3 publications

A Novel Robust Model for Health Care Facilities Location-Allocation Considering Pre Disaster and Post Disaster Characteristics

A Novel Robust Model for Health Care Facilities Location-Allocation Considering Pre Disaster and Post Disaster Characteristics

Optimizing the Pre-Hospital Mobile Care System: A Case Study

Decision Support Systems in Health Care

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