A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways

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

doi:10.1007/s10479-007-0195-z

Cited by 62 publications

(28 citation statements)

References 26 publications

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“…• New call arrival (lines [8][9][10][11][12][13][14][15] • Scene departure (lines [16][17][18][19][20][21][22][23]. The vehicles assigned to a call are leaving the scene of the incident, t sc seconds after arrival of the rst vehicle.…”

Section: Simulation Modelmentioning

confidence: 99%

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

McCormack

Coates

2015

European Journal of Operational Research

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(2015) 'A simulation model to enable the optimization of ambulance eet allocation and base station location for increased patient survival.', European journal of operational research., 247 (1). pp. 294-309. Further information on publisher's website:http://dx.doi.org/10.1016/j.ejor.2015.05.040Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reected in this document. Changes may have been made to this work since it was submitted for publication. A denitive version was subsequently published in European Journal of Operational Research, 247, 1, 2015Research, 247, 1, , 10.1016Research, 247, 1, /j.ejor.2015 Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. further improved survival when its location and resourcing were optimized for key periods of service. Also, the removal of a base station from the system was found to have minimal impact on survival probability when the selected station and resourcing were optimized simultaneously.

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Section: Simulation Modelmentioning

confidence: 99%

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

McCormack

Coates

2015

European Journal of Operational Research

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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%

“…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%

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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“…It is not difficult to envisage a situation where the 'best' single location may not be included in the set of N best locations when the whole set is evaluated rather than the individual. Additionally, much of the previous research relating specifically to ambulance location problems have considered a small number of locations: Chuang (2007) used to a GA to identify only 4 facilities; Iannoni et al (2008) combined a GA within a hypercube to consider the optimal location of five ambulances on two connected stretches of highway.…”

Section: Introductionmentioning

confidence: 99%

A modified grouping genetic algorithm to select ambulance site locations

Comber

Sasaki

Sano

et al. 2011

International Journal of Geographical Information Science

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This paper describes the development and application of a modified Grouping Genetic Algorithm (GGA) used to identify sets of optimal ambulance locations. The GGA was modified to consider a special case with only two groups, and the reproduction and mutation schemes were modified to operate more efficiently. It was applied to a case study locating ambulances from a fixed set of alternative locations. The sites were evaluated using data of emergency medical services (EMS) calls summarised over census areas and weighted by network distance. Census areas serviced by the same selected location defined ambulance catchments. The results indicated alternative sites for ambulances to be located, with average EMS response times improved by 1 minute 14 seconds and showed the impacts of having different numbers of ambulances in current locations and in new locations. The algorithmic developments associated with the modified GGA and the advantages of using census areas as spatial units to summarise data are discussed.

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A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways

Cited by 62 publications

References 26 publications

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

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

A modified grouping genetic algorithm to select ambulance site locations

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