Ambulance deployment analysis: A case study of Bangkok

Fujiwara, Okitsugu; Makjamroen, Thanet; Gupta, Kapil Kumar

doi:10.1016/0377-2217(87)90130-5

Cited by 76 publications

(27 citation statements)

References 13 publications

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“…Fujiwara et al [21] analysed deployment policies for Bangkok, Thailand, combining a simulation model informed by a location model (a mathematical programming model).…”

Section: Literature Reviewmentioning

confidence: 99%

A generic method to develop simulation models for ambulance systems

Pinto

Silva

2015

Simulation Modelling Practice and Theory

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In this paper, we address the question of generic simulation models and their role in improving emergency care around the world. After reviewing the development of ambulance models and the contexts in which they have been applied, we report the construction of a reusable model for ambulance systems. Further, we describe the associated parameters, data sources, and performance measures, and report on the collection of information, as well as the use of optimisation to configure the service to best effect. Having developed the model, we have validated it using real data from the emergency medical system in a Brazilian city, Belo Horizonte. To illustrate the benefits of standardisation and reusability we apply the model to a UK context by exploring how different rules of engagement would change the performance of the system. Finally, we consider the impact that one might observe if such rules were adopted by the Brazilian system.

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“…Fujiwara et al [21] analysed deployment policies for Bangkok, Thailand, combining a simulation model informed by a location model (a mathematical programming model).…”

Section: Literature Reviewmentioning

confidence: 99%

A generic method to develop simulation models for ambulance systems

Pinto

Silva

2015

Simulation Modelling Practice and Theory

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“…For instance, Eaton et al (1985) saved $3.4 million in construction costs in Austin (Texas), while, in Bangkok, Fujiwara et al (1987 reduced the total number of ambulances from 21 to 15, keeping the average response time and, more recently, Takeda et al (2004) reduced the average travel time by only repositioning ambulances.…”

Section: Introductionmentioning

confidence: 99%

SAMU ambulance positioning using MALP model

Coelho

Alexandrino

Barreto

2017

BJO&PM

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One of the constitutional principles of the public health care system in Brazil is universality, which turns health into a fundamental right and ensures that all citizens shall have access to health service whenever required. The purpose of this study is to assess the positioning of ambulances in Duque de Caxias-RJ, and find new arrangements to maximize the covered population. The configuration of a network that provides such service is indeed significant since small deviations may lead to users' death. Therefore, four scenarios were built in order to represent different network arrangements, according to the manager's strategy or the budget limitations of the city. An Integer Programming model for servers' positioning was used in each scenario. Indicators such as percentage of coverage population and total cost were then used to compare and choose the best solution. Results have shown that the current coverage could be doubled by just relocating facilities that already exist, without adding any costs. It is important to notice that this solution is rather different from the current positioning.

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“…Other factors to take into account in planning the locations of ambulances are the likelihood of timing and location of an emergency, staff availability, location constraints (e.g., a place where staff can rest), and the emergency care center where patients are potentially transported to [36,62,79,158,180,205,267,276,399,412,526]. Methods: computer simulation [62,165,180,186,204,237,267,412,429,459,526], heuristics [22,36,38,164,197,266], Markov processes [21,257], mathematical programming [18,36,38,39,40,45,79,118,158,165,186,205,207,237,257,276,412,…”

Section: Strategic Planningmentioning

confidence: 99%

“…Ambulances exist in different transport modalities (helicopters, vans, cars) carrying different types of equipment and staff [38,39,40,165,186,267,412,429,446,468,526]. Ambulances collect emergency patients, but also perform less urgent transfers of patients between care facilities [468].…”

Section: Ambulance Districtingmentioning

confidence: 99%

“…For example, staff members may be 'on call' while working elsewhere or being off-duty, and they are called upon when additional staff is required in the ED to cope with unexpected demand peaks [376]. Methods: computer simulation [16,40,60,91,165,181,186,267,311,315,375,412,429,526,532], heuristics [38], mathematical programming [38,39,165,375,376,412], queueing theory [106,220,446,468], literature review [62,282,393].…”

Section: Ambulance Districtingmentioning

confidence: 99%

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Integrated decision making in healthcare : an operations research and management science perspective

Hulshof¹

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Ambulance deployment analysis: A case study of Bangkok

Cited by 76 publications

References 13 publications

A generic method to develop simulation models for ambulance systems

A generic method to develop simulation models for ambulance systems

SAMU ambulance positioning using MALP model

Integrated decision making in healthcare : an operations research and management science perspective

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