A modeling framework for facility location of medical services for large-scale emergencies

Jia, Hongzhong; Ordóñez, Fernando; Dessouky, Maged

doi:10.1080/07408170500539113

Cited by 378 publications

(207 citation statements)

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“…It has been reviewed thoroughly by Swersey (1994), Marianov and Revelle (1995), and more recently by Brotcorne et al (2003) and Jia et al (2007). We limit our discussion of this literature to papers that are most relevant to this paper, namely those that use generalizations of covering models where numbers other than 0 or 1 are used to quantify the quality of coverage, as well as approaches to minimize average response time.…”

Section: Literature Reviewmentioning

confidence: 99%

Ambulance location for maximum survival

Erkut

Ingolfsson

Erdoğan

2007

Naval Research Logistics

193

139

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This paper proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an EMS vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures-the expected number of survivors in case of cardiac arrests. The survival-maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival-maximizing models using data from the Edmonton EMS system.

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Section: Literature Reviewmentioning

confidence: 99%

Ambulance location for maximum survival

Erkut

Ingolfsson

Erdoğan

2007

Naval Research Logistics

193

139

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“…The model assumes that public health centres also provide paid services to highincome patients who are willing to be allocated to a public health centre if it is closer than any other private health centre. Jia et al (2007) analyse the problem of locating medical facilities that are primarily used in case of a largescale emergency such as earthquakes, terrorist attacks, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Matching patient and physician preferences in designing a primary care facility network

Güneş

Yaman

Çekyay

et al. 2014

Journal of the Operational Research Society

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This paper introduces an integer programming model for planning primary care facility networks, which accounts for the interests of different stakeholders while maximizing access to health care. Physician allocation to health-care facilities is explicitly modelled, which allows consideration of physician incentives in the planning phase. An illustrative case study in the Turkish primary care system is presented to show the implications of focusing on patient or physician preferences in the planning phase. A discussion of trade-offs between the different stakeholder preferences and some recommendations for modelling choices to match these preferences are provided. In the context of this case, we found that using an access measure that decays with distance, and incorporating nearest allocation constraints improves performance for all stakeholders. We also show that increasing the number of physicians may have adverse affects on access measures when physician preferences are addressed.

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“…At present, the approaches proposed for solving large-scale ERFLs problem are generally exact approaches, such as linear, nonlinear or other mathematical programming methods (Jia et al 2007a;Maliszewski and Horner 2010;Horner and Downs 2010;Maliszewski et al 2012;Bell et al 2011). Unfortunately, there are practical limitations associated with the use of exact methods in the large-scale ERFLs problem, as it is actually a geographical optimization problem.…”

mentioning

confidence: 99%

“…(Chrissis 1980;Revelle and Snyder 1995;Liu et al 2006;Alçada-Almeida et al 2009;Indriasari et al 2010;Maliszewski and Horner 2010), as well as the ones for large-scale disasters emergency services (e.g., earthquakes, floods, hurricanes, etc.) (Jia et al 2007a(Jia et al , 2007bHorner and Downs 2010;Bell et al 2011;Caunhye et al 2012;Maliszewski et al 2012). However, models proposed for environmental accidents emergency services are sparse, which are actually much more frequent hazardous events than nature disasters in urban system.…”

mentioning

confidence: 99%

“…The essential issue of emergency rescue facilities locations (ERFLs) problem is actually a facility location optimization problem (Church and ReVelle 1974;Revelle and Hogan 1989;Jia et al 2007a;Church and Murray 2009). Research on facility location problem is abundant, and many decision models have been developed to solve various facility location problems, including the ones in the context of regular emergency services (e.g., fire stations, medical centers, etc.)…”

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confidence: 99%

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Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety

Zhao

Chen

2014

Nat Hazards

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Emergency rescue facility is an indispensable component of urban system and important for public safety. Due to the special characteristics and complexity of urban environmental accidents, locations of emergency rescue facilities should be optimized, and the decision criteria should be comprehensive, taking into account not only the spatial relations of these facilities but also the potential hazard of environmental accidents. This study proposes an innovative methodological framework to optimize the locations of emergency rescue facilities in the contexts of large-scale urban environmental accidents. Risk-based approach is involved to delineate the potential hazard of environmental accidents to the adjacent urban areas, and a three-objective decision optimization model is constructed. Moreover, an appropriate spatial representation and encoding strategy is designed and coupled with the non-dominated sorting genetic algorithm-II for model solving. A case study is presented to demonstrate the methodology, and it provides evidence that the involvement of risk mapping into the model construction has significant effect on the optimization result. The findings show that the strategy proposed in this study has the potential to be a useful decision support tool for urban planning with respect to public safety.

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A modeling framework for facility location of medical services for large-scale emergencies

Cited by 378 publications

References 50 publications

Ambulance location for maximum survival

Ambulance location for maximum survival

Matching patient and physician preferences in designing a primary care facility network

Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety

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