An optimization approach for ambulance location and the districting of the response segments on highways

Iannoni, Ana Paula; Morábito, Reinaldo; Saydam, Cem

doi:10.1016/j.ejor.2008.02.003

Cited by 103 publications

(49 citation statements)

References 23 publications

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“…Ambulance services have been analysed in a variety of ways, including queuing theory and hypercube models [3,4,5,6,7,8,9], mixed integer programming [10,11], stochastic optimization [12] and dynamic programming [13]. By contrast, simulation modelling presents a high cost of implementation, but gives a more detailed description of the system [3] and allows the analysis of dynamic effects [14,15].…”

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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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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“…A practical example of such a system is the SAU-Centrovias case studied in Iannoni et al (2008Iannoni et al ( , 2009). The flow equations of the system can be built in a way similar to the single partial backup model, and by taking into account multiple dispatch transitions.…”

Section: The Basic Modelmentioning

confidence: 99%

“…The model considers a zero capacity waiting line system with homogeneous or non-homogeneous servers. This is the case of some emergency medical systems on motorways, as for example in the SAU's (Sistema de Atendimento ao Usuário -highway's user response system) studied in Mendonça & Morabito (2001), Iannoni & Morabito (2007) and Iannoni et al (2009Iannoni et al ( , 2011.…”

Section: Introductionmentioning

confidence: 99%

“…Some examples of applications of the hypercube queueing model include the location of ambulances in urban areas (Brandeau & Larson, 1986;Burwell et al, 1993;Takeda et al, 2007;Rajagopalan et al, 2008) and ambulances and service patrol vehicles on highways (Mendonça and Morabito, 2001; Iannoni et al, 2008Iannoni et al, , 2009Geroliminis et al, 2009Geroliminis et al, , 2011; the deployment of police patrol units (Chelst and Barlach, 1981;Larson and Macknew, 1982;Sacks and Grief, 1994); the design of repair services related to interruptions in the distribution of electrical energy (Albino, 1994), and the programs for visits by the social service (Larson & Odoni, 1981). The hypercube has also been considered as a deployment model for response to terrorism attacks and other major emergencies (Larson, 2004).…”

Section: Introductionmentioning

confidence: 99%

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A tutorial on hypercube queueing models and some practical applications in Emergency Service Systems

Chiyoshi¹,

Iannoni

Morábito

2011

Pesqui. Oper.

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ABSTRACT. This paper presents some extensions and applications of hypercube queueing models to describe server-to-customer type Emergency Service Systems. The classical hypercube is a well-known spatially distributed queueing model effective in analyzing these systems, based on Markovian analysis approximations. Experience has shown that each real life Emergency Service System may have its own unique characteristics so that each system may require a particular hypercube queueing model incorporating those characteristics. Some of these distinctive characteristics are considered in the extensions presented in this tutorial such as dispatch policy based on random selection of the server to take an incoming call; partial cooperation among servers whereby depending on where the call is coming from, some servers cannot take the call; servers with additional workload coming from walk-in nonemergency customers such as in customer-to-server systems; existence of calls requiring the dispatch of more than one server; existence of more than one type of servers in the system, for instance paramedical and medical units in emergency medical systems. In this study, we present a set of these models based on the smallest non-trivial service systems. For each model, the construction of the system of equations for equilibrium hypercube state probabilities and the evaluation of particular operational characteristics are described.

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“…Despite not being an optimization model, it gives an acceptable structure of facilities. Studies of Takeda et al (2007), Galvão and Morabito (2008), Iannoni et al (2009) andToro-Diaz et al (2013) are appropriate applications of hypercube model to find out more.…”

Section: Related Workmentioning

confidence: 99%

An ILP and simulation model to optimize search and rescue helicopter operations

Karataş

Razi

Günal

2017

Journal of the Operational Research Society

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Maritime search and rescue (SAR) operations, conducted for rendering aid to the victims in need of help at sea, play a crucial role in dropping the number of causalities. Therefore, it is of high importance to organize SAR operations properly. In this paper, we compose a hybrid methodology which combines optimization and simulation to allocate SAR helicopters. First, we build an integer linear programming (ILP) model to provide an effective deployment plan and use it as an input to a simulation model which includes constraints that the ILP model cannot tackle. Next, using a rule-based algorithm, we generate alternative solutions and seek better plans that exist in the vicinity of the ILP model solution. We perform our methodology on the historical incident data in the Aegean Sea region. Results show that the hybrid methodology we adopted leads to a more effective utilization of resources than the optimization model alone.

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An optimization approach for ambulance location and the districting of the response segments on highways

Cited by 103 publications

References 23 publications

A generic method to develop simulation models for ambulance systems

A generic method to develop simulation models for ambulance systems

A tutorial on hypercube queueing models and some practical applications in Emergency Service Systems

An ILP and simulation model to optimize search and rescue helicopter operations

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