Computational Comparison of Five Maximal Covering Models for Locating Ambulances

Erkut, Erhan; Ingolfsson, Armann; Sim, Thaddeus; Erdoğan, Güneş

doi:10.1111/j.1538-4632.2009.00747.x

Cited by 48 publications

(31 citation statements)

References 22 publications

(37 reference statements)

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“…In particular, we assume that the RT is lognormally distributed, as commonly reported in the literature [13].…”

Section: The Ems Casementioning

confidence: 99%

“…Moreover, constraints (9,10), where δ is a non-Archimedean number, guarantee that only non-dominated efficient solutions in the DEA model are investigated. Finally, restrictions (11)(12)(13) define the nature of decision variables. Our model resembles the Klimberg's and Ratick's [16] model, even though some distinguishing features can be highlighted.…”

Section: Designing An Equitable and Efficient Systemmentioning

confidence: 99%

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Balancing efficiency and equity in location-allocation models with an application to strategic EMS design

et al. 2015

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This paper presents an integrated location-allocation model balancing efficiency and equity criteria. The new formulation combines two domains: facility location and data envelopment analysis. To support the decision maker with more realistic solutions based on the optimal location-allocation decisions, we endogenize the outputs of the model as a function dependent on the allocation variables. To illustrate the viability of the proposed approach, we investigated the potential application of the model to the design of an emergency medical service system.

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“…In particular, we assume that the RT is lognormally distributed, as commonly reported in the literature [13].…”

Section: The Ems Casementioning

confidence: 99%

Section: Designing An Equitable and Efficient Systemmentioning

confidence: 99%

Balancing efficiency and equity in location-allocation models with an application to strategic EMS design

et al. 2015

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“…Erkut et al [22] solve the non-linear model for 180 demand points and 16 bases, but note that finding optimal solution for instances with more bases would be problematic. To apply the model to determine optimal base locations rather than an optimal distribution of the ambulances given a fixed set of bases, we need to solve instances with more base locations.…”

Section: Figmentioning

confidence: 99%

Linear formulation for the Maximum Expected Coverage Location Model with fractional coverage

Berg

Kommer

Zuzáková

2016

Operations Research for Health Care

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a b s t r a c tSince ambulance providers are responsible for life-saving medical care at the scene in emergency situations and since response times are important in these situations, it is crucial that ambulances are located in such a way that good coverage is provided throughout the region. Most models that are developed to determine good base locations assume strict 0-1 coverage given a fixed base location and demand point. However, multiple applications require fractional coverage. Examples include stochastic, instead of fixed, response times and survival probabilities. Straightforward adaption of the well-studied MEXCLP to allow for coverage probabilities results in a non-linear formulation in integer variables, limiting the size of instances that can be solved by the model. In this paper, we present a linear integer programming formulation for the problem. We show that the computation time of the linear formulation is significantly shorter than that for the non-linear formulation. As a consequence, we are able to solve larger instances. Finally, we will apply the model, in the setting of stochastic response times, to the region of Amsterdam, the Netherlands.

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“…Problem (MALP) [11,12], Queuing Maximum Availability Location Problem (Q-MALP) [10], and Multiserver Queuing Maximum Availability Location Problem (MQ-MALP) [12] models. In all covering models, the aim is to find a set of optimal locations of facilities, such as ambulances, that can cover all or a maximum number of demand points, where coverage is defined similarly in some models while differently in others.…”

Section: Journal Of Computational Engineeringmentioning

confidence: 99%

Analysis of MCLP, Q-MALP, and MQ-MALP with Travel Time Uncertainty Using Monte Carlo Simulation

Ghani

Ahmad

2017

Journal of Computational Engineering

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This paper compares the application of the Monte Carlo simulation in incorporating travel time uncertainties in ambulance location problem using three models: Maximum Covering Location Problem (MCLP), Queuing Maximum Availability Location Problem (Q-MALP), and Multiserver Queuing Maximum Availability Location Problem (MQ-MALP). A heuristic method is developed to site the ambulances. The models are applied to the 33-node problem representing Austin, Texas, and the 55-node problem. For the 33-node problem, the results show that the servers are less spatially distributed in Q-MALP and MQ-MALP when the uncertainty of server availability is considered using either the independent or dependent travel time. On the other hand, for the 55-node problem, the spatial distribution of the servers obtained by locating a server to the highest hit node location is more dispersed in MCLP and Q-MALP. The implications of the new model for the ambulance services system design are discussed as well as the limitations of the modeling approach.

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Computational Comparison of Five Maximal Covering Models for Locating Ambulances

Cited by 48 publications

References 22 publications

Balancing efficiency and equity in location-allocation models with an application to strategic EMS design

Balancing efficiency and equity in location-allocation models with an application to strategic EMS design

Linear formulation for the Maximum Expected Coverage Location Model with fractional coverage

Analysis of MCLP, Q-MALP, and MQ-MALP with Travel Time Uncertainty Using Monte Carlo Simulation

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